{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ai4learning/miniconda3/envs/wxy-cognitive/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "from run import run\n",
    "import time\n",
    "\n",
    "class Config:\n",
    "    pass\n",
    "\n",
    "def objective(trial, model, dataset):\n",
    "    cfg = Config()\n",
    "    batch_size = trial.suggest_int('batch_size', 16, 128)\n",
    "    learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n",
    "\n",
    "    setattr(cfg, \"epoch\", 10)\n",
    "    setattr(cfg, \"batch_size\", batch_size)\n",
    "    setattr(cfg, \"lr\", learning_rate)\n",
    "    setattr(cfg, \"model\", model)\n",
    "    setattr(cfg, \"dataset\", dataset)\n",
    "    setattr(cfg, \"early_stop\", True)\n",
    "    setattr(cfg, \"save\", False)\n",
    "    setattr(cfg, \"less_data\", False)\n",
    "    setattr(cfg, \"device\", \"auto\")\n",
    "    setattr(cfg, \"seed\", int(time.time()))\n",
    "    result = run(cfg)\n",
    "    return result[f\"{model} on {dataset}\"][\"auc\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2025-01-28 20:30:38,126] A new study created in memory with name: no-name-11efe114-de64-45c6-addf-8fbe57ca0087\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000286\n",
      "Using device: cuda\n",
      "Seed: 1738067438\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 95\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1078/1078 [00:03<00:00, 313.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 723.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.0682835334528353, AUC: 0.5268175222975454, ACC: 0.5140907089387935, RMSE: 0.5053247935659277\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1078/1078 [00:03<00:00, 308.12it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 702.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6950559817351304, AUC: 0.5534748573768861, ACC: 0.5140907089387935, RMSE: 0.5007151939314102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1078/1078 [00:02<00:00, 360.35it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 729.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6932976396826954, AUC: 0.588887325718646, ACC: 0.5140907089387935, RMSE: 0.5007802921326668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1078/1078 [00:03<00:00, 292.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 710.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6929913478348826, AUC: 0.6210660799495817, ACC: 0.5140907089387935, RMSE: 0.5008795509534095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1078/1078 [00:03<00:00, 271.62it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 707.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6927796106280998, AUC: 0.644569724454052, ACC: 0.5140907089387935, RMSE: 0.5009935129430282\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1078/1078 [00:03<00:00, 282.76it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 725.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6927865775706374, AUC: 0.656479001071688, ACC: 0.5140907089387935, RMSE: 0.5009668901487737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1078/1078 [00:03<00:00, 345.13it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 730.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6928202776386919, AUC: 0.6624768745133285, ACC: 0.5140907089387935, RMSE: 0.5009678219451935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1078/1078 [00:03<00:00, 303.23it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 730.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6926307512899941, AUC: 0.6653005528987052, ACC: 0.5140907089387935, RMSE: 0.5009942984570901\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1078/1078 [00:03<00:00, 322.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 729.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6924787106230883, AUC: 0.6669093030298228, ACC: 0.5140907089387935, RMSE: 0.5009708118504813\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1078/1078 [00:03<00:00, 291.59it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 144/144 [00:00<00:00, 710.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6922673402779177, AUC: 0.6680253419003687, ACC: 0.5140907089387935, RMSE: 0.500940820793829\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 288/288 [00:00<00:00, 703.68it/s]\n",
      "[I 2025-01-28 20:31:19,813] Trial 0 finished with value: 0.6680253419003687 and parameters: {'batch_size': 95, 'learning_rate': 0.0002861188642436658}. Best is trial 0 with value: 0.6680253419003687.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.015065\n",
      "Using device: cuda\n",
      "Seed: 1738067479\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 113\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 907/907 [00:02<00:00, 336.00it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 656.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7561064218935919, AUC: 0.6815113721713222, ACC: 0.6226332012329371, RMSE: 0.4819698831450011\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 907/907 [00:03<00:00, 281.10it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 646.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.5698322787174448, AUC: 0.6788273765227569, ACC: 0.6219726992514311, RMSE: 0.5043313998541474\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 907/907 [00:03<00:00, 264.43it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 653.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.4299185334847544, AUC: 0.6645232368490757, ACC: 0.6170556289446646, RMSE: 0.521167169523555\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 907/907 [00:02<00:00, 303.85it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 649.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.3926527829062452, AUC: 0.662084173806851, ACC: 0.6169822398356084, RMSE: 0.524570442639156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 907/907 [00:02<00:00, 328.79it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 654.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.3685838850882508, AUC: 0.6584043201282102, ACC: 0.6150741230001467, RMSE: 0.5296557390030773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 907/907 [00:02<00:00, 338.92it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 656.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.35636415072155103, AUC: 0.6537783064527345, ACC: 0.5967268457360928, RMSE: 0.5437380730739926\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 242/242 [00:00<00:00, 653.49it/s]\n",
      "[I 2025-01-28 20:31:40,386] Trial 1 finished with value: 0.6537783064527345 and parameters: {'batch_size': 113, 'learning_rate': 0.01506513328630713}. Best is trial 0 with value: 0.6680253419003687.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.003449\n",
      "Using device: cuda\n",
      "Seed: 1738067500\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 96\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1067/1067 [00:03<00:00, 352.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 725.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9279350091501609, AUC: 0.6594280888324854, ACC: 0.5140907089387935, RMSE: 0.5037108266383559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1067/1067 [00:03<00:00, 299.77it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 724.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6562003989921291, AUC: 0.6759753239023499, ACC: 0.6067077645677381, RMSE: 0.4856716494632251\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1067/1067 [00:03<00:00, 320.14it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 721.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6040715752300416, AUC: 0.6899062000829882, ACC: 0.6354029062087186, RMSE: 0.47976752853482485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1067/1067 [00:04<00:00, 238.52it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 705.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5475057295637926, AUC: 0.69428153515392, ACC: 0.6381916923528549, RMSE: 0.4848920377761682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1067/1067 [00:04<00:00, 240.79it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 704.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.4616854138595542, AUC: 0.6847740857852737, ACC: 0.631806839864964, RMSE: 0.5009225030388715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1067/1067 [00:04<00:00, 241.06it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 700.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3958787224705016, AUC: 0.674547869177959, ACC: 0.6241743725231176, RMSE: 0.5164242765043586\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1067/1067 [00:04<00:00, 240.86it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 713.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.36527086286033106, AUC: 0.6683469021900661, ACC: 0.6193306913254073, RMSE: 0.5261225317930707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1067/1067 [00:04<00:00, 265.63it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 726.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.35181643115574773, AUC: 0.6653437071778945, ACC: 0.6238074269778365, RMSE: 0.5367986911490519\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1067/1067 [00:03<00:00, 297.38it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 142/142 [00:00<00:00, 719.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.34133782305500726, AUC: 0.661231081732135, ACC: 0.62160575370615, RMSE: 0.5370276173748517\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 285/285 [00:00<00:00, 719.40it/s]\n",
      "[I 2025-01-28 20:32:19,280] Trial 2 finished with value: 0.661231081732135 and parameters: {'batch_size': 96, 'learning_rate': 0.0034493150379004945}. Best is trial 0 with value: 0.6680253419003687.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001237\n",
      "Using device: cuda\n",
      "Seed: 1738067539\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 23\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 4453/4453 [00:14<00:00, 308.03it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 593/593 [00:00<00:00, 1360.66it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8120671236432057, AUC: 0.6659649325934041, ACC: 0.4859092910612065, RMSE: 0.5033416015191965\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 4453/4453 [00:14<00:00, 310.39it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6787887139318232, AUC: 0.6704087345209762, ACC: 0.5688389842947307, RMSE: 0.48946411280093904\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 4453/4453 [00:14<00:00, 309.26it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6314568843114464, AUC: 0.6784825627281352, ACC: 0.6271099368853662, RMSE: 0.47695584127221113\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 4453/4453 [00:17<00:00, 253.26it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6070402866029054, AUC: 0.6864552484303345, ACC: 0.6313665052106268, RMSE: 0.47328505426098916\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 4453/4453 [00:17<00:00, 248.88it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5820693713130776, AUC: 0.6925527119871764, ACC: 0.6359900190811684, RMSE: 0.4727919032097087\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 4453/4453 [00:15<00:00, 279.46it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5488773029412202, AUC: 0.6954573194691992, ACC: 0.6399530309702041, RMSE: 0.4756280254347078\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 4453/4453 [00:16<00:00, 272.25it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5034967417007427, AUC: 0.6935609567938665, ACC: 0.6373844121532365, RMSE: 0.48383544379323234\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 4453/4453 [00:14<00:00, 307.60it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.4540300452519127, AUC: 0.6866941763068453, ACC: 0.6329076765008073, RMSE: 0.4977775569285349\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 4453/4453 [00:15<00:00, 286.27it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.41074951371487983, AUC: 0.6794195593132012, ACC: 0.6298987230295024, RMSE: 0.5101759589744208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 4453/4453 [00:16<00:00, 277.64it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 593/593 [00:00<00:00, 1354.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.37926283425028445, AUC: 0.6724964076730745, ACC: 0.6252752091589608, RMSE: 0.5195579342944954\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 1186/1186 [00:00<00:00, 1358.18it/s]\n",
      "[I 2025-01-28 20:35:02,949] Trial 3 finished with value: 0.6724964076730745 and parameters: {'batch_size': 23, 'learning_rate': 0.0012369438873996129}. Best is trial 3 with value: 0.6724964076730745.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000034\n",
      "Using device: cuda\n",
      "Seed: 1738067702\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 47\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2179/2179 [00:07<00:00, 295.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 7.522852331061411, AUC: 0.510640176429096, ACC: 0.5140907089387935, RMSE: 0.6563219918759672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2179/2179 [00:07<00:00, 293.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.9501667569514095, AUC: 0.5122831545982918, ACC: 0.5140907089387935, RMSE: 0.5304707509434686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2179/2179 [00:08<00:00, 271.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.7149927341073629, AUC: 0.5125000148231662, ACC: 0.5140907089387935, RMSE: 0.5040072409006455\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2179/2179 [00:07<00:00, 297.04it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6947863736848537, AUC: 0.5166172327552945, ACC: 0.5140907089387935, RMSE: 0.5008938012733818\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2179/2179 [00:07<00:00, 290.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6925852364112938, AUC: 0.5273510269112156, ACC: 0.5140907089387935, RMSE: 0.5004874604383203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2179/2179 [00:08<00:00, 252.65it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6925257708075507, AUC: 0.5441973449607327, ACC: 0.5140907089387935, RMSE: 0.5004737825378999\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2179/2179 [00:08<00:00, 262.26it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6923433703640942, AUC: 0.5629892278165389, ACC: 0.5140907089387935, RMSE: 0.5004447462802886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2179/2179 [00:07<00:00, 287.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.692357909548113, AUC: 0.5795303072297918, ACC: 0.5140907089387935, RMSE: 0.5004043286878728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2179/2179 [00:07<00:00, 296.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6922117891307269, AUC: 0.5936975239706768, ACC: 0.5140907089387935, RMSE: 0.5004136116749756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2179/2179 [00:07<00:00, 292.96it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6920668978984338, AUC: 0.6052983709286557, ACC: 0.5140907089387935, RMSE: 0.5004120403442602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 581/581 [00:00<00:00, 1054.81it/s]\n",
      "[I 2025-01-28 20:36:24,627] Trial 4 finished with value: 0.6052983709286557 and parameters: {'batch_size': 47, 'learning_rate': 3.350620416605482e-05}. Best is trial 3 with value: 0.6724964076730745.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000068\n",
      "Using device: cuda\n",
      "Seed: 1738067784\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 92\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1114/1114 [00:04<00:00, 258.53it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 8.060697517479825, AUC: 0.504188272105033, ACC: 0.5140907089387935, RMSE: 0.6486163109490731\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.9257078417702252, AUC: 0.504592087490962, ACC: 0.5140907089387935, RMSE: 0.5318442069139889\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.7214434639127721, AUC: 0.5062044922063027, ACC: 0.5140907089387935, RMSE: 0.5064111976760969\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6976647188676324, AUC: 0.5078269874550736, ACC: 0.5140907089387935, RMSE: 0.5015404814679361\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.693599771554423, AUC: 0.5136248721299789, ACC: 0.5140907089387935, RMSE: 0.5005888484610416\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.693054215965836, AUC: 0.5241295163087707, ACC: 0.5140907089387935, RMSE: 0.5004517840189273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6928648999499247, AUC: 0.5396671805723596, ACC: 0.5140907089387935, RMSE: 0.5004410371164421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6926082741850368, AUC: 0.559315109355617, ACC: 0.5140907089387935, RMSE: 0.5004253436320864\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1114/1114 [00:04<00:00, 266.87it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.692580871832435, AUC: 0.5800608903321802, ACC: 0.5140907089387935, RMSE: 0.5003535966043462\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1114/1114 [00:04<00:00, 271.26it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6924437274303642, AUC: 0.5980130596944531, ACC: 0.5140907089387935, RMSE: 0.5003876272545245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 297/297 [00:00<00:00, 740.88it/s]\n",
      "[I 2025-01-28 20:37:12,821] Trial 5 finished with value: 0.5980130596944531 and parameters: {'batch_size': 92, 'learning_rate': 6.821260952964387e-05}. Best is trial 3 with value: 0.6724964076730745.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000405\n",
      "Using device: cuda\n",
      "Seed: 1738067832\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 120\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 854/854 [00:03<00:00, 270.51it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.199537076771399, AUC: 0.538495482060681, ACC: 0.5140907089387935, RMSE: 0.5046052789901618\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6941405333037678, AUC: 0.5720426894247006, ACC: 0.5140907089387935, RMSE: 0.5006906658885764\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 854/854 [00:03<00:00, 236.28it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6927410195945856, AUC: 0.6093465290775, ACC: 0.5140907089387935, RMSE: 0.501031153117524\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 854/854 [00:03<00:00, 236.66it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6927133915854282, AUC: 0.6354389452115299, ACC: 0.5140907089387935, RMSE: 0.5012055744561783\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 854/854 [00:03<00:00, 242.96it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6928463689197701, AUC: 0.6523260889555985, ACC: 0.5140907089387935, RMSE: 0.5013280291716433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 854/854 [00:03<00:00, 253.59it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6928146032734275, AUC: 0.6616694701316438, ACC: 0.5140907089387935, RMSE: 0.5014567836817496\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 854/854 [00:03<00:00, 243.37it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6926937034174765, AUC: 0.6654183620326, ACC: 0.5140907089387935, RMSE: 0.5013419724199694\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 854/854 [00:03<00:00, 233.59it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6925647006101854, AUC: 0.6672649943332383, ACC: 0.5140907089387935, RMSE: 0.5013287661125128\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 854/854 [00:03<00:00, 234.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6920441819019005, AUC: 0.6685401100320925, ACC: 0.5140907089387935, RMSE: 0.5013011335141327\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 854/854 [00:03<00:00, 238.48it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6915520874203228, AUC: 0.6694561924773563, ACC: 0.5140907089387935, RMSE: 0.5010589736345363\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 228/228 [00:00<00:00, 597.06it/s]\n",
      "[I 2025-01-28 20:37:52,107] Trial 6 finished with value: 0.6694561924773563 and parameters: {'batch_size': 120, 'learning_rate': 0.00040490926678863416}. Best is trial 3 with value: 0.6724964076730745.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001032\n",
      "Using device: cuda\n",
      "Seed: 1738067872\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 56\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1829/1829 [00:06<00:00, 273.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.994371351451415, AUC: 0.6452839099868359, ACC: 0.5140907089387935, RMSE: 0.5058988216541329\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1829/1829 [00:06<00:00, 280.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6976271073077792, AUC: 0.6673286530938212, ACC: 0.5140907089387935, RMSE: 0.5050811768606766\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1829/1829 [00:06<00:00, 270.34it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6915761726583008, AUC: 0.6692631247816277, ACC: 0.5140907089387935, RMSE: 0.5027495313057135\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1829/1829 [00:06<00:00, 288.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6657267010003377, AUC: 0.6713964964072419, ACC: 0.5454278585057978, RMSE: 0.49420103027564166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1829/1829 [00:06<00:00, 293.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.629717005576639, AUC: 0.6767154580611665, ACC: 0.5962131219726993, RMSE: 0.4851518140178245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1829/1829 [00:06<00:00, 275.67it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6073551020901212, AUC: 0.6828329948800245, ACC: 0.6260824893585792, RMSE: 0.4804281545981797\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1829/1829 [00:06<00:00, 278.23it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.590057190354891, AUC: 0.6883868417287973, ACC: 0.6337883458094818, RMSE: 0.4770987116072454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1829/1829 [00:07<00:00, 244.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5695024475235311, AUC: 0.6926776362407978, ACC: 0.6400264200792602, RMSE: 0.47634313864329925\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1829/1829 [00:07<00:00, 244.65it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.546776551311299, AUC: 0.6952471647056426, ACC: 0.638925583443417, RMSE: 0.4775371624957139\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1829/1829 [00:07<00:00, 250.61it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 244/244 [00:00<00:00, 938.03it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5188529070216231, AUC: 0.6959082779135579, ACC: 0.6395860854249229, RMSE: 0.479793855954101\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 488/488 [00:00<00:00, 954.00it/s]\n",
      "[I 2025-01-28 20:39:04,592] Trial 7 finished with value: 0.6959082779135579 and parameters: {'batch_size': 56, 'learning_rate': 0.0010323930049099212}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000012\n",
      "Using device: cuda\n",
      "Seed: 1738067944\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 66\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1552/1552 [00:05<00:00, 298.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 27.50876887945324, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970704453420482\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1552/1552 [00:04<00:00, 323.21it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 4.665974486945677, AUC: 0.49276925957800216, ACC: 0.5140907089387935, RMSE: 0.6965514715091999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1552/1552 [00:03<00:00, 426.90it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 2.626920346314683, AUC: 0.49479258401851395, ACC: 0.5140907089387935, RMSE: 0.6878914925405093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1552/1552 [00:03<00:00, 426.40it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 1.6779034887008446, AUC: 0.496158536079209, ACC: 0.5140907089387935, RMSE: 0.6582499301652914\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1552/1552 [00:05<00:00, 294.99it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 1.1831537727842625, AUC: 0.4973561616559513, ACC: 0.5140907089387935, RMSE: 0.6105238806159132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1552/1552 [00:04<00:00, 324.65it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.9203391848612077, AUC: 0.4986158828230337, ACC: 0.5140907089387935, RMSE: 0.5629527057623792\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 414/414 [00:00<00:00, 851.23it/s]\n",
      "[I 2025-01-28 20:39:35,129] Trial 8 finished with value: 0.4986158828230337 and parameters: {'batch_size': 66, 'learning_rate': 1.2469597129427194e-05}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.010270\n",
      "Using device: cuda\n",
      "Seed: 1738067975\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 42\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2439/2439 [00:05<00:00, 456.18it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 47.11548442085926, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2439/2439 [00:05<00:00, 408.22it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 47.11630499055025, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2439/2439 [00:07<00:00, 311.66it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 47.11630499055025, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2439/2439 [00:06<00:00, 354.87it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 47.11630499055025, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2439/2439 [00:08<00:00, 300.26it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 325/325 [00:00<00:00, 1054.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 47.11630499055025, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2439/2439 [00:09<00:00, 259.64it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 325/325 [00:00<00:00, 1056.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 47.11630499055025, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 650/650 [00:00<00:00, 1094.34it/s]\n",
      "[I 2025-01-28 20:40:22,355] Trial 9 finished with value: 0.5 and parameters: {'batch_size': 42, 'learning_rate': 0.010269719598197658}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.061057\n",
      "Using device: cuda\n",
      "Seed: 1738068022\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 9\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 11378/11378 [00:29<00:00, 390.50it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1659.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 47.12386306769049, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 11378/11378 [00:21<00:00, 521.76it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1688.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 47.134089698982606, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 11378/11378 [00:34<00:00, 328.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1624.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 47.134089698982606, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 11378/11378 [00:32<00:00, 352.63it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1623.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 47.134089698982606, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 11378/11378 [00:42<00:00, 268.76it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1657.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 47.134089698982606, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 11378/11378 [00:39<00:00, 289.52it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1514/1514 [00:00<00:00, 1661.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 47.134089698982606, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970719410944659\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 3031/3031 [00:01<00:00, 1665.38it/s]\n",
      "[I 2025-01-28 20:43:50,449] Trial 10 finished with value: 0.5 and parameters: {'batch_size': 9, 'learning_rate': 0.061057257233359245}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001498\n",
      "Using device: cuda\n",
      "Seed: 1738068230\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 10\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 10240/10240 [00:28<00:00, 355.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1363/1363 [00:00<00:00, 1647.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7208688455957116, AUC: 0.6692419842516526, ACC: 0.5215763980625275, RMSE: 0.495942453670431\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 10240/10240 [00:29<00:00, 344.03it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1363/1363 [00:00<00:00, 1637.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.632116738869081, AUC: 0.6798045843147617, ACC: 0.5928372229561133, RMSE: 0.48111487057183894\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 10240/10240 [00:31<00:00, 320.02it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1363/1363 [00:00<00:00, 1655.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6043929490053415, AUC: 0.6878836626178402, ACC: 0.6077352120945252, RMSE: 0.47798637589689846\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 10240/10240 [00:31<00:00, 326.81it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5721279779783799, AUC: 0.6936223732136872, ACC: 0.6209452517246441, RMSE: 0.4787425717358528\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 10240/10240 [00:33<00:00, 307.92it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1363/1363 [00:00<00:00, 1620.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.517930604769208, AUC: 0.69015796751646, ACC: 0.6222662556876559, RMSE: 0.48770720410241813\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 10240/10240 [00:36<00:00, 281.29it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.45503222926781745, AUC: 0.6810695879192167, ACC: 0.6147805665639219, RMSE: 0.5040148623274071\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 10240/10240 [00:32<00:00, 310.57it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.4083306486313177, AUC: 0.6736246608324841, ACC: 0.6194040804344635, RMSE: 0.5149455106773336\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 10240/10240 [00:33<00:00, 307.58it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3755769719167802, AUC: 0.6666438336006355, ACC: 0.6174959635990019, RMSE: 0.5235051686457994\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 10240/10240 [00:34<00:00, 295.30it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 1363/1363 [00:00<00:00, 1658.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3574632137251001, AUC: 0.662739638040923, ACC: 0.6187435784529576, RMSE: 0.5282378777097453\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 2728/2728 [00:01<00:00, 1671.34it/s]\n",
      "[I 2025-01-28 20:48:53,349] Trial 11 finished with value: 0.662739638040923 and parameters: {'batch_size': 10, 'learning_rate': 0.0014982983105212215}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000436\n",
      "Using device: cuda\n",
      "Seed: 1738068533\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 32\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 3200/3200 [00:06<00:00, 502.23it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 426/426 [00:00<00:00, 1210.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0939507865533233, AUC: 0.6132855024799966, ACC: 0.5140907089387935, RMSE: 0.5031793840481157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 3200/3200 [00:06<00:00, 501.20it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6962754444684833, AUC: 0.661043598327343, ACC: 0.5140907089387935, RMSE: 0.5028972976597038\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 3200/3200 [00:06<00:00, 498.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6959430450294167, AUC: 0.6668580094848858, ACC: 0.5140907089387935, RMSE: 0.5024980047838042\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 3200/3200 [00:06<00:00, 491.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.694904591171071, AUC: 0.6686440015605829, ACC: 0.5140907089387935, RMSE: 0.5022885913000191\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 3200/3200 [00:10<00:00, 304.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.692096540601924, AUC: 0.6696208751575703, ACC: 0.5140907089387935, RMSE: 0.5013075144741761\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 3200/3200 [00:09<00:00, 348.86it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.685099116479978, AUC: 0.6702968330925512, ACC: 0.5140907089387935, RMSE: 0.499736855054966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 3200/3200 [00:11<00:00, 289.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6716897713486105, AUC: 0.6712328163120803, ACC: 0.5267136356964627, RMSE: 0.49557374767359536\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 3200/3200 [00:12<00:00, 246.48it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 426/426 [00:00<00:00, 1218.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6511633089743555, AUC: 0.6728374051762065, ACC: 0.5581975634815793, RMSE: 0.4899508335034088\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 3200/3200 [00:10<00:00, 292.24it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 426/426 [00:00<00:00, 1167.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6317337616253644, AUC: 0.6752018133637258, ACC: 0.5852047556142669, RMSE: 0.48469839794449054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 3200/3200 [00:10<00:00, 311.14it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 426/426 [00:00<00:00, 1207.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6162493984214962, AUC: 0.678089969826502, ACC: 0.6164685160722149, RMSE: 0.4808343301137197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 853/853 [00:00<00:00, 1214.48it/s]\n",
      "[I 2025-01-28 20:50:29,595] Trial 12 finished with value: 0.678089969826502 and parameters: {'batch_size': 32, 'learning_rate': 0.0004363849943957935}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000197\n",
      "Using device: cuda\n",
      "Seed: 1738068629\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 56\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1829/1829 [00:05<00:00, 305.17it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.945463753206082, AUC: 0.5276479645744656, ACC: 0.5140907089387935, RMSE: 0.504055328512045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6943995472575355, AUC: 0.5656069342663196, ACC: 0.5140907089387935, RMSE: 0.5011583292766728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1829/1829 [00:03<00:00, 461.43it/s]\n",
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    },
    {
     "name": "stdout",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6931441222923971, AUC: 0.6054797957011955, ACC: 0.5140907089387935, RMSE: 0.5011944272673233\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1829/1829 [00:03<00:00, 461.21it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6930683181545264, AUC: 0.6379905996331832, ACC: 0.5140907089387935, RMSE: 0.501118283099066\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 4: 100%|██████████| 1829/1829 [00:03<00:00, 462.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6929704788410318, AUC: 0.654805039790229, ACC: 0.5140907089387935, RMSE: 0.5013017301416413\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1829/1829 [00:05<00:00, 328.68it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6929397005536897, AUC: 0.6610294651122501, ACC: 0.5140907089387935, RMSE: 0.5012118145548948\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1829/1829 [00:06<00:00, 262.09it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6929083269042614, AUC: 0.6641077677594736, ACC: 0.5140907089387935, RMSE: 0.5011902702523637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1829/1829 [00:07<00:00, 257.27it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.692737105311169, AUC: 0.6660910534807974, ACC: 0.5140907089387935, RMSE: 0.5011247238826072\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1829/1829 [00:07<00:00, 243.93it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6926176886926133, AUC: 0.6673689613251199, ACC: 0.5140907089387935, RMSE: 0.5012279565461984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1829/1829 [00:07<00:00, 254.55it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 244/244 [00:00<00:00, 749.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6924879884641636, AUC: 0.6682879113792433, ACC: 0.5140907089387935, RMSE: 0.5010213037419557\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 488/488 [00:00<00:00, 750.21it/s]\n",
      "[I 2025-01-28 20:51:30,735] Trial 13 finished with value: 0.6682879113792433 and parameters: {'batch_size': 56, 'learning_rate': 0.00019688296873804736}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.004113\n",
      "Using device: cuda\n",
      "Seed: 1738068690\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 33\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 3103/3103 [00:11<00:00, 268.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7231037799116492, AUC: 0.6746939555225241, ACC: 0.5852781447233231, RMSE: 0.48358838862749415\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 3103/3103 [00:11<00:00, 274.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6167357896667429, AUC: 0.690348016676547, ACC: 0.6349625715543813, RMSE: 0.47373277794585983\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 3103/3103 [00:10<00:00, 285.14it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.5357863274680542, AUC: 0.6884696250687661, ACC: 0.6358432408630559, RMSE: 0.4948082423172989\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 3103/3103 [00:12<00:00, 254.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.425815900575815, AUC: 0.6736708875497371, ACC: 0.6262292675766916, RMSE: 0.5240559446566454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 3103/3103 [00:12<00:00, 239.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.3682362674127711, AUC: 0.6637312162186783, ACC: 0.6203581388521944, RMSE: 0.5293299829356602\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 3103/3103 [00:09<00:00, 323.34it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.34475998693697163, AUC: 0.6569768761843034, ACC: 0.6092763833847057, RMSE: 0.5362017145553365\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 3103/3103 [00:06<00:00, 456.18it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.33359284854231114, AUC: 0.6529881508461441, ACC: 0.6061206516952884, RMSE: 0.5390157654771359\n",
      "[NCDM] Early stopping at epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 827/827 [00:00<00:00, 1220.53it/s]\n",
      "[I 2025-01-28 20:52:51,198] Trial 14 finished with value: 0.6529881508461441 and parameters: {'batch_size': 33, 'learning_rate': 0.004113491358609907}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000108\n",
      "Using device: cuda\n",
      "Seed: 1738068771\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 76\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1348/1348 [00:03<00:00, 429.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 3.965309795213294, AUC: 0.5141879152709983, ACC: 0.5140907089387935, RMSE: 0.549678230303631\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1348/1348 [00:03<00:00, 420.20it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.7208761118728259, AUC: 0.5164116273561692, ACC: 0.5140907089387935, RMSE: 0.5029859628323132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1348/1348 [00:05<00:00, 250.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6936001129426306, AUC: 0.5236561236763048, ACC: 0.5140907089387935, RMSE: 0.5005692195306064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1348/1348 [00:05<00:00, 241.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6923679827051984, AUC: 0.5389904680034683, ACC: 0.5140907089387935, RMSE: 0.5004946833571994\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6922174334437627, AUC: 0.5643167474502268, ACC: 0.5140907089387935, RMSE: 0.5004671080136286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1348/1348 [00:05<00:00, 238.22it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6921639056870774, AUC: 0.5948997528142723, ACC: 0.5140907089387935, RMSE: 0.5004838747430403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1348/1348 [00:05<00:00, 234.84it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6922495969590519, AUC: 0.6196450180524602, ACC: 0.5140907089387935, RMSE: 0.5004764855627284\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1348/1348 [00:05<00:00, 233.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6922413508686892, AUC: 0.6357454667254419, ACC: 0.5140907089387935, RMSE: 0.5005037640557054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1348/1348 [00:05<00:00, 236.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6921508405523413, AUC: 0.6458932553084993, ACC: 0.5140907089387935, RMSE: 0.5004844941569999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1348/1348 [00:05<00:00, 238.59it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 180/180 [00:00<00:00, 805.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6921822609551229, AUC: 0.6521217664341208, ACC: 0.5140907089387935, RMSE: 0.5004809677866511\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 359/359 [00:00<00:00, 825.64it/s]\n",
      "[I 2025-01-28 20:53:46,769] Trial 15 finished with value: 0.6521217664341208 and parameters: {'batch_size': 76, 'learning_rate': 0.00010797584755008815}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000578\n",
      "Using device: cuda\n",
      "Seed: 1738068826\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 28\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 3658/3658 [00:13<00:00, 270.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9034168528929606, AUC: 0.632535502021826, ACC: 0.5140907089387935, RMSE: 0.5003183169067676\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 3658/3658 [00:14<00:00, 254.34it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.697085122240248, AUC: 0.6611082920144316, ACC: 0.5140907089387935, RMSE: 0.5004991889858922\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 3658/3658 [00:15<00:00, 241.87it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6957962642754277, AUC: 0.665773643677607, ACC: 0.5140907089387935, RMSE: 0.5005015795366842\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6893613517626574, AUC: 0.6675219903016606, ACC: 0.5140907089387935, RMSE: 0.49851183695234563\n"
     ]
    },
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    },
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6708386898236304, AUC: 0.6690893757140048, ACC: 0.541097901071481, RMSE: 0.49273039063145746\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 3658/3658 [00:15<00:00, 236.20it/s]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6427637844006829, AUC: 0.6721892501105807, ACC: 0.576104506091296, RMSE: 0.48582027254522064\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 3658/3658 [00:15<00:00, 240.09it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6205722054597979, AUC: 0.676495493056775, ACC: 0.6164685160722149, RMSE: 0.4804651311109338\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 3658/3658 [00:15<00:00, 243.46it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6060929705229465, AUC: 0.6810463559752614, ACC: 0.6261558784676354, RMSE: 0.4786820787404542\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 3658/3658 [00:15<00:00, 243.23it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5938714410663629, AUC: 0.6854701601042084, ACC: 0.6315132834287391, RMSE: 0.47621832097010547\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 3658/3658 [00:14<00:00, 258.15it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5810881038265127, AUC: 0.6892757250118343, ACC: 0.6367972992807868, RMSE: 0.47513825558112677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 975/975 [00:01<00:00, 937.14it/s]\n",
      "[I 2025-01-28 20:56:18,737] Trial 16 finished with value: 0.6892757250118343 and parameters: {'batch_size': 28, 'learning_rate': 0.0005779111955234675}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000888\n",
      "Using device: cuda\n",
      "Seed: 1738068978\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 65\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1576/1576 [00:03<00:00, 396.71it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.095716894202426, AUC: 0.619221000038702, ACC: 0.5140907089387935, RMSE: 0.5050665682879056\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1576/1576 [00:05<00:00, 302.49it/s]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6965861868132189, AUC: 0.6593272697420587, ACC: 0.5140907089387935, RMSE: 0.5055172927585468\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1576/1576 [00:06<00:00, 242.23it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6953310058050349, AUC: 0.6653973293074692, ACC: 0.5140907089387935, RMSE: 0.504901060724112\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1576/1576 [00:04<00:00, 329.85it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6890306113486363, AUC: 0.6677496956938758, ACC: 0.5140907089387935, RMSE: 0.5027169878669868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1576/1576 [00:06<00:00, 237.27it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6683810006089622, AUC: 0.6699807492889462, ACC: 0.5364743872009394, RMSE: 0.4956036886430054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1576/1576 [00:06<00:00, 238.73it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6364687310560101, AUC: 0.6741059663405247, ACC: 0.579113459562601, RMSE: 0.48720023156608877\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1576/1576 [00:05<00:00, 292.19it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6129451634617626, AUC: 0.6792853638429667, ACC: 0.6208718626155878, RMSE: 0.48091522044709184\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1576/1576 [00:06<00:00, 235.89it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5964298949288536, AUC: 0.6844804038283225, ACC: 0.6288712755027154, RMSE: 0.4786931805381935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1576/1576 [00:06<00:00, 236.22it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5805910779588719, AUC: 0.6890137484596035, ACC: 0.6342286804638192, RMSE: 0.47604439853927083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1576/1576 [00:06<00:00, 236.24it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5637314007972097, AUC: 0.692530892286682, ACC: 0.6384852487890798, RMSE: 0.47601675051734893\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 420/420 [00:00<00:00, 708.73it/s]\n",
      "[I 2025-01-28 20:57:22,643] Trial 17 finished with value: 0.692530892286682 and parameters: {'batch_size': 65, 'learning_rate': 0.0008880024946049496}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002979\n",
      "Using device: cuda\n",
      "Seed: 1738069042\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 78\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1313/1313 [00:03<00:00, 354.34it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8098021297625522, AUC: 0.6601928671787182, ACC: 0.5140907089387935, RMSE: 0.4987753274021195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1313/1313 [00:04<00:00, 293.85it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6565437074027167, AUC: 0.67431376017799, ACC: 0.5929106120651695, RMSE: 0.489243731804686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1313/1313 [00:03<00:00, 419.34it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6084507492236844, AUC: 0.6880136752601143, ACC: 0.6287978863936592, RMSE: 0.4817867167093998\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.560523431762338, AUC: 0.6944197840863017, ACC: 0.6373844121532365, RMSE: 0.4827840440274131\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1313/1313 [00:05<00:00, 255.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.4868755831758393, AUC: 0.6878564634556131, ACC: 0.6315132834287391, RMSE: 0.4965927654269276\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1313/1313 [00:05<00:00, 242.89it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.41547129464939364, AUC: 0.677794897187059, ACC: 0.6269631586672538, RMSE: 0.512373458064319\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1313/1313 [00:05<00:00, 250.55it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.371515167027326, AUC: 0.6699032807278034, ACC: 0.6202847497431381, RMSE: 0.5232278163455708\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1313/1313 [00:04<00:00, 268.83it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3496212478207888, AUC: 0.6660230394045032, ACC: 0.617862909144283, RMSE: 0.530633658132751\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1313/1313 [00:04<00:00, 266.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3397321536036184, AUC: 0.6619130443969456, ACC: 0.6136797299280786, RMSE: 0.5342094062858995\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 350/350 [00:00<00:00, 626.47it/s]\n",
      "[I 2025-01-28 20:58:07,749] Trial 18 finished with value: 0.6619130443969456 and parameters: {'batch_size': 78, 'learning_rate': 0.00297892878496813}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.014359\n",
      "Using device: cuda\n",
      "Seed: 1738069087\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 58\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1766/1766 [00:06<00:00, 285.84it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.6983840757156148, AUC: 0.6846541097740078, ACC: 0.6299721121385586, RMSE: 0.4792440913952034\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1766/1766 [00:07<00:00, 229.07it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.542834612970174, AUC: 0.6729812923019471, ACC: 0.6171290180537208, RMSE: 0.5125373090800286\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1766/1766 [00:07<00:00, 230.38it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.4168026201502923, AUC: 0.658495749416695, ACC: 0.6128724497284603, RMSE: 0.5293733350883227\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1766/1766 [00:07<00:00, 233.67it/s]\n",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.3856016849700281, AUC: 0.6579551404637829, ACC: 0.6134595626009101, RMSE: 0.5278417679587756\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.37365642467583293, AUC: 0.6528830950253347, ACC: 0.6092763833847057, RMSE: 0.5349494037745463\n"
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1766/1766 [00:07<00:00, 242.96it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 235/235 [00:00<00:00, 726.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3588454618476255, AUC: 0.6507641369074089, ACC: 0.5926170556289446, RMSE: 0.54457887082523\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 471/471 [00:00<00:00, 728.89it/s]\n",
      "[I 2025-01-28 20:58:55,478] Trial 19 finished with value: 0.6507641369074089 and parameters: {'batch_size': 58, 'learning_rate': 0.014358891426281096}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.090669\n",
      "Using device: cuda\n",
      "Seed: 1738069135\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 83\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1234/1234 [00:04<00:00, 281.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 165/165 [00:00<00:00, 610.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7371290161408713, AUC: 0.672670943716061, ACC: 0.5140907089387935, RMSE: 0.491333570677705\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1234/1234 [00:04<00:00, 277.40it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 165/165 [00:00<00:00, 754.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.534978520022509, AUC: 0.6431377850481366, ACC: 0.5877733744312345, RMSE: 0.5162776829789116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1234/1234 [00:05<00:00, 240.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.4759482705733185, AUC: 0.6446315720932498, ACC: 0.60259797446059, RMSE: 0.5526705029454689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1234/1234 [00:05<00:00, 234.88it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5176548071483352, AUC: 0.6388819085252179, ACC: 0.5981946279172171, RMSE: 0.5419261363710896\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1234/1234 [00:04<00:00, 290.78it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.508582885940129, AUC: 0.6341847328719933, ACC: 0.5926904447380009, RMSE: 0.5236965100011821\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1234/1234 [00:03<00:00, 341.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 165/165 [00:00<00:00, 614.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.505958111349922, AUC: 0.6365148914604657, ACC: 0.5984147952443857, RMSE: 0.5363343050438469\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 329/329 [00:00<00:00, 608.04it/s]\n",
      "[I 2025-01-28 20:59:25,688] Trial 20 finished with value: 0.6365148914604657 and parameters: {'batch_size': 83, 'learning_rate': 0.09066928063348917}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000770\n",
      "Using device: cuda\n",
      "Seed: 1738069165\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 52\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1970/1970 [00:07<00:00, 258.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.276096964729619, AUC: 0.6288823084811903, ACC: 0.5140907089387935, RMSE: 0.505231672687879\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1970/1970 [00:08<00:00, 237.58it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6968948842910341, AUC: 0.6627589782299975, ACC: 0.5140907089387935, RMSE: 0.5051103343031375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1970/1970 [00:08<00:00, 232.26it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6956879179489794, AUC: 0.6672002144022745, ACC: 0.5140907089387935, RMSE: 0.5037467674071291\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1970/1970 [00:07<00:00, 263.78it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6896136769485958, AUC: 0.66895671107256, ACC: 0.5140907089387935, RMSE: 0.5023281532417049\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1970/1970 [00:08<00:00, 240.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6714567261601463, AUC: 0.6706664527818555, ACC: 0.5299427564949362, RMSE: 0.4962884309470229\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1970/1970 [00:08<00:00, 238.13it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6417961438900323, AUC: 0.6739828963302261, ACC: 0.5692793189490679, RMSE: 0.4884990219169614\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1970/1970 [00:08<00:00, 235.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6177650625784386, AUC: 0.6785599881673403, ACC: 0.6111111111111112, RMSE: 0.4822240087148342\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1970/1970 [00:08<00:00, 242.98it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6009214903194893, AUC: 0.6834259646458325, ACC: 0.6263760457948041, RMSE: 0.47951365530353374\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1970/1970 [00:08<00:00, 238.70it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5871691242238591, AUC: 0.687835419949998, ACC: 0.6329810656098634, RMSE: 0.47644223425436005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1970/1970 [00:08<00:00, 244.24it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 263/263 [00:00<00:00, 970.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5719679054118655, AUC: 0.6914484561861169, ACC: 0.6366505210626743, RMSE: 0.47591217116871615\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 525/525 [00:00<00:00, 988.54it/s]\n",
      "[I 2025-01-28 21:00:52,081] Trial 21 finished with value: 0.6914484561861169 and parameters: {'batch_size': 52, 'learning_rate': 0.0007701342923338439}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001026\n",
      "Using device: cuda\n",
      "Seed: 1738069252\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 63\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1626/1626 [00:06<00:00, 243.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0167599887865495, AUC: 0.6361588336205793, ACC: 0.5140907089387935, RMSE: 0.5062303263465582\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1626/1626 [00:06<00:00, 241.21it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6970758286532734, AUC: 0.6636803431126342, ACC: 0.5140907089387935, RMSE: 0.50598016088137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1626/1626 [00:06<00:00, 237.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6932743612543977, AUC: 0.6674486398855717, ACC: 0.5140907089387935, RMSE: 0.5044331491354249\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1626/1626 [00:06<00:00, 252.53it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6749455578542723, AUC: 0.6698384468944173, ACC: 0.5278144723323059, RMSE: 0.497880972482411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1626/1626 [00:06<00:00, 251.30it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6390065176900462, AUC: 0.6743213604195161, ACC: 0.5804344635256128, RMSE: 0.4870737748340326\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1626/1626 [00:06<00:00, 252.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6125609060201844, AUC: 0.6803927567649966, ACC: 0.619917804197857, RMSE: 0.481535317110769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1626/1626 [00:06<00:00, 237.55it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5950218411425969, AUC: 0.6861673879349778, ACC: 0.6312931161015706, RMSE: 0.47847247256897685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1626/1626 [00:06<00:00, 237.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5766629300287582, AUC: 0.6909422908809715, ACC: 0.6353295170996625, RMSE: 0.47736073131301715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1626/1626 [00:06<00:00, 255.05it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 217/217 [00:00<00:00, 728.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5553537638351633, AUC: 0.6943104699741409, ACC: 0.6414208131513284, RMSE: 0.4766260841695776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1626/1626 [00:06<00:00, 261.68it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 217/217 [00:00<00:00, 714.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5311821712705629, AUC: 0.6958281681337288, ACC: 0.6378247468075737, RMSE: 0.4786758082281469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 433/433 [00:00<00:00, 709.56it/s]\n",
      "[I 2025-01-28 21:02:02,934] Trial 22 finished with value: 0.6958281681337288 and parameters: {'batch_size': 63, 'learning_rate': 0.0010258038508697788}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002146\n",
      "Using device: cuda\n",
      "Seed: 1738069322\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 63\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1626/1626 [00:05<00:00, 273.48it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8550829149480236, AUC: 0.6588642263746491, ACC: 0.5140907089387935, RMSE: 0.49911709813498173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6752831242694479, AUC: 0.6697417998514665, ACC: 0.5595185674445913, RMSE: 0.4938961469017193\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1626/1626 [00:06<00:00, 255.98it/s]\n",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6233703009646757, AUC: 0.6816876007503648, ACC: 0.6230001467782181, RMSE: 0.4814266926802534\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 3: 100%|██████████| 1626/1626 [00:06<00:00, 241.37it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5917435242241159, AUC: 0.6911213008249981, ACC: 0.63518273888155, RMSE: 0.4793816703772426\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1626/1626 [00:06<00:00, 261.06it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5496721693107269, AUC: 0.6954462263507166, ACC: 0.6353295170996625, RMSE: 0.48127760645563183\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1626/1626 [00:06<00:00, 263.20it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.49000047271908576, AUC: 0.691590144093033, ACC: 0.6363569646264494, RMSE: 0.49097919219374403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1626/1626 [00:06<00:00, 243.50it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.42906743376933926, AUC: 0.6826383101116309, ACC: 0.6293116101570527, RMSE: 0.5058045237225136\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1626/1626 [00:06<00:00, 255.21it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3841007137338907, AUC: 0.6741168869712564, ACC: 0.6274034933215911, RMSE: 0.5177189395274243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1626/1626 [00:05<00:00, 316.11it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3565257280460143, AUC: 0.6683718266700776, ACC: 0.6235138705416117, RMSE: 0.5262221834077303\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1626/1626 [00:05<00:00, 304.44it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 217/217 [00:00<00:00, 897.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.33924746293881575, AUC: 0.6638660046155566, ACC: 0.617862909144283, RMSE: 0.5310447445606946\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 433/433 [00:00<00:00, 904.93it/s]\n",
      "[I 2025-01-28 21:03:08,264] Trial 23 finished with value: 0.6638660046155566 and parameters: {'batch_size': 63, 'learning_rate': 0.002146226562258573}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.007877\n",
      "Using device: cuda\n",
      "Seed: 1738069388\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 71\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1443/1443 [00:05<00:00, 242.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7264345555733411, AUC: 0.6784006418269213, ACC: 0.6174225744899456, RMSE: 0.4826881995168635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1443/1443 [00:06<00:00, 238.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6027996187109296, AUC: 0.6941342629560671, ACC: 0.6361367972992807, RMSE: 0.48012169965893786\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1443/1443 [00:05<00:00, 242.22it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.469870265198927, AUC: 0.6731491121031312, ACC: 0.6239542051959489, RMSE: 0.51673035831005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1443/1443 [00:05<00:00, 243.83it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.39142833634106056, AUC: 0.6650741088231689, ACC: 0.6223396447967122, RMSE: 0.5285266009432629\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1443/1443 [00:05<00:00, 243.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.36583643836728913, AUC: 0.6619315005862966, ACC: 0.6138265081461911, RMSE: 0.5310218347276785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1443/1443 [00:05<00:00, 242.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3524731313570952, AUC: 0.6579570378290434, ACC: 0.6084691031850873, RMSE: 0.5355545163779847\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1443/1443 [00:06<00:00, 237.67it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3428205365724268, AUC: 0.6537427847565245, ACC: 0.6128724497284603, RMSE: 0.5385358714135757\n",
      "[NCDM] Early stopping at epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 385/385 [00:00<00:00, 848.98it/s]\n",
      "[I 2025-01-28 21:03:53,475] Trial 24 finished with value: 0.6537427847565245 and parameters: {'batch_size': 71, 'learning_rate': 0.00787672465072792}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000146\n",
      "Using device: cuda\n",
      "Seed: 1738069433\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 41\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2498/2498 [00:09<00:00, 259.82it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.08491844606934, AUC: 0.5238535143462913, ACC: 0.5140907089387935, RMSE: 0.5030686559316867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2498/2498 [00:10<00:00, 241.95it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6935879072253469, AUC: 0.5595028730530041, ACC: 0.5140907089387935, RMSE: 0.5011577927310403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2498/2498 [00:10<00:00, 241.90it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6930428273540387, AUC: 0.6023911006669778, ACC: 0.5140907089387935, RMSE: 0.5012336091160035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2498/2498 [00:10<00:00, 241.75it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6928707343086994, AUC: 0.6344310992827636, ACC: 0.5140907089387935, RMSE: 0.5012896349910871\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6927688249451719, AUC: 0.6503734844929739, ACC: 0.5140907089387935, RMSE: 0.5011937510551936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2498/2498 [00:10<00:00, 241.28it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.692767073790296, AUC: 0.6578659642965449, ACC: 0.5140907089387935, RMSE: 0.5012713026662686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2498/2498 [00:10<00:00, 241.16it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6928005756092988, AUC: 0.66185572456121, ACC: 0.5140907089387935, RMSE: 0.5012510377125631\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2498/2498 [00:10<00:00, 240.93it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6926384979815746, AUC: 0.6643092873549984, ACC: 0.5140907089387935, RMSE: 0.5011791336655635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2498/2498 [00:10<00:00, 240.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6925782379243162, AUC: 0.6659055536851415, ACC: 0.5140907089387935, RMSE: 0.5010419456279622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2498/2498 [00:07<00:00, 347.58it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6924660134974053, AUC: 0.6669838932016218, ACC: 0.5140907089387935, RMSE: 0.5011964227338205\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 666/666 [00:00<00:00, 898.63it/s]\n",
      "[I 2025-01-28 21:05:38,404] Trial 25 finished with value: 0.6669838932016218 and parameters: {'batch_size': 41, 'learning_rate': 0.00014623310663183054}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000864\n",
      "Using device: cuda\n",
      "Seed: 1738069538\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 86\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1191/1191 [00:03<00:00, 331.65it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.1130003184955928, AUC: 0.6100991578177756, ACC: 0.5140907089387935, RMSE: 0.502910736671954\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1191/1191 [00:03<00:00, 332.75it/s]\n",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6951116750362438, AUC: 0.6546393006225406, ACC: 0.5140907089387935, RMSE: 0.5036849313685668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1191/1191 [00:05<00:00, 233.58it/s]\n",
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     ]
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6950033230505301, AUC: 0.6650633606801882, ACC: 0.5140907089387935, RMSE: 0.5037540673890113\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1191/1191 [00:05<00:00, 236.28it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6930970151218219, AUC: 0.6676683892802743, ACC: 0.5140907089387935, RMSE: 0.5032667270802249\n"
     ]
    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6859647875869104, AUC: 0.6692388040087447, ACC: 0.5140907089387935, RMSE: 0.5011085710955655\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1191/1191 [00:05<00:00, 231.28it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6673188349161701, AUC: 0.6710564799281934, ACC: 0.5360340525466021, RMSE: 0.4948757311514625\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1191/1191 [00:05<00:00, 231.04it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6391958132899178, AUC: 0.6742904419901593, ACC: 0.576104506091296, RMSE: 0.4865938963966264\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1191/1191 [00:05<00:00, 233.53it/s]\n",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.615395764009378, AUC: 0.6786490673102183, ACC: 0.615808014090709, RMSE: 0.48143526427695016\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1191/1191 [00:05<00:00, 232.16it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5986004749033053, AUC: 0.6832170280770171, ACC: 0.6276236606487597, RMSE: 0.4786497591597799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1191/1191 [00:05<00:00, 235.92it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5834149768830947, AUC: 0.6875963303662206, ACC: 0.6332746220460883, RMSE: 0.4767448609091206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 318/318 [00:00<00:00, 648.47it/s]\n",
      "[I 2025-01-28 21:06:30,832] Trial 26 finished with value: 0.6875963303662206 and parameters: {'batch_size': 86, 'learning_rate': 0.0008641700000558335}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.005885\n",
      "Using device: cuda\n",
      "Seed: 1738069590\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 108\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 949/949 [00:03<00:00, 280.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8462372866269786, AUC: 0.6666159552678892, ACC: 0.5585645090268604, RMSE: 0.49796380118349\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 949/949 [00:03<00:00, 240.15it/s]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6251520033380129, AUC: 0.6879149044617299, ACC: 0.6293849992661089, RMSE: 0.48178564907183846\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 949/949 [00:04<00:00, 235.21it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.5522676611788281, AUC: 0.6903609963798054, ACC: 0.6346690151181564, RMSE: 0.49170365242037656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 949/949 [00:04<00:00, 234.89it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.4397220685842542, AUC: 0.6737135998290646, ACC: 0.6191839131072949, RMSE: 0.5146692143948179\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 949/949 [00:04<00:00, 234.94it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.38320502800547035, AUC: 0.6677723239307026, ACC: 0.6169088507265522, RMSE: 0.5262294792384207\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 949/949 [00:04<00:00, 234.39it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.361372648539106, AUC: 0.6640545229468541, ACC: 0.6218259210333187, RMSE: 0.5353767299897301\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 949/949 [00:04<00:00, 235.06it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3463526274400466, AUC: 0.6599181696548553, ACC: 0.614487010127697, RMSE: 0.5359027446723663\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 949/949 [00:04<00:00, 233.25it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.33879770647863944, AUC: 0.6571012398527343, ACC: 0.593424335828563, RMSE: 0.5507378658671385\n",
      "[NCDM] Early stopping at epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 253/253 [00:00<00:00, 536.11it/s]\n",
      "[I 2025-01-28 21:07:06,096] Trial 27 finished with value: 0.6571012398527343 and parameters: {'batch_size': 108, 'learning_rate': 0.0058850588965718405}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001567\n",
      "Using device: cuda\n",
      "Seed: 1738069626\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 64\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1600/1600 [00:06<00:00, 264.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0240784101001918, AUC: 0.6519912686700472, ACC: 0.5140907089387935, RMSE: 0.5058509358639891\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1600/1600 [00:06<00:00, 235.26it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6950961748324335, AUC: 0.6664478012716876, ACC: 0.5140907089387935, RMSE: 0.5043271938297725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1600/1600 [00:06<00:00, 238.42it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6584942896291613, AUC: 0.6723196292893256, ACC: 0.566930867459269, RMSE: 0.49051934735449215\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1600/1600 [00:06<00:00, 236.12it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6171948841959238, AUC: 0.6812204715793551, ACC: 0.6186701893439014, RMSE: 0.4831398517047842\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5937407066486776, AUC: 0.6888022569159773, ACC: 0.6323205636283575, RMSE: 0.4801927946730717\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1600/1600 [00:06<00:00, 235.82it/s]\n",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5643253445252776, AUC: 0.6939598347179249, ACC: 0.636210186408337, RMSE: 0.4799477709924284\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5269211448729039, AUC: 0.695766816396815, ACC: 0.6375311903713489, RMSE: 0.4818169987574693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1600/1600 [00:05<00:00, 280.98it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.48087057139724493, AUC: 0.6929844703887583, ACC: 0.6373844121532365, RMSE: 0.4897614952738934\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1600/1600 [00:04<00:00, 386.52it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.4365973134804517, AUC: 0.6870073493796531, ACC: 0.6326875091736386, RMSE: 0.500249695651182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1600/1600 [00:06<00:00, 242.70it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.39859037173911926, AUC: 0.6805177349210401, ACC: 0.6290180537208279, RMSE: 0.5093810356249998\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 427/427 [00:00<00:00, 912.23it/s]\n",
      "[I 2025-01-28 21:08:13,323] Trial 28 finished with value: 0.6805177349210401 and parameters: {'batch_size': 64, 'learning_rate': 0.0015668543746591565}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000242\n",
      "Using device: cuda\n",
      "Seed: 1738069693\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 73\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1403/1403 [00:03<00:00, 412.80it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.8844323994980823, AUC: 0.5166948845846726, ACC: 0.5140907089387935, RMSE: 0.5049926170670095\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6940233874388958, AUC: 0.5483218612808229, ACC: 0.5140907089387935, RMSE: 0.5008246209038328\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6927253791713239, AUC: 0.5876998014558181, ACC: 0.5140907089387935, RMSE: 0.5008654781198162\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6927604442057402, AUC: 0.6225546708011981, ACC: 0.5140907089387935, RMSE: 0.5010504434755987\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6926738993236191, AUC: 0.6449448314099333, ACC: 0.5140907089387935, RMSE: 0.5010659670829762\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6927403743914511, AUC: 0.6547219977186338, ACC: 0.5140907089387935, RMSE: 0.5010660187675446\n"
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1403/1403 [00:03<00:00, 427.55it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6927652750283756, AUC: 0.6597730643343735, ACC: 0.5140907089387935, RMSE: 0.5010317285538466\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1403/1403 [00:03<00:00, 428.14it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6925946504238751, AUC: 0.66275521584093, ACC: 0.5140907089387935, RMSE: 0.5009592075957853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1403/1403 [00:03<00:00, 429.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.692571936041138, AUC: 0.6647309228816968, ACC: 0.5140907089387935, RMSE: 0.5009648575373128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1403/1403 [00:03<00:00, 367.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6923527781740734, AUC: 0.6662173684169107, ACC: 0.5140907089387935, RMSE: 0.5010224945466583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 374/374 [00:00<00:00, 839.72it/s]\n",
      "[I 2025-01-28 21:08:50,749] Trial 29 finished with value: 0.6662173684169107 and parameters: {'batch_size': 73, 'learning_rate': 0.000242320995863462}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.038810\n",
      "Using device: cuda\n",
      "Seed: 1738069730\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 100\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1024/1024 [00:02<00:00, 390.88it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.72529354353901, AUC: 0.6801397819172681, ACC: 0.6336415675913695, RMSE: 0.4809914244671448\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1024/1024 [00:02<00:00, 390.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.5186273880826775, AUC: 0.6586666955583651, ACC: 0.6098634962571554, RMSE: 0.5114885479426731\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1024/1024 [00:02<00:00, 389.77it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.42255046295031207, AUC: 0.6554939019650775, ACC: 0.6147805665639219, RMSE: 0.5300667907221891\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1024/1024 [00:02<00:00, 388.39it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.407952061665128, AUC: 0.6552115502972664, ACC: 0.6143402319095846, RMSE: 0.5302475077120032\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1024/1024 [00:02<00:00, 388.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.3982877660309896, AUC: 0.6500452942053494, ACC: 0.5956260091002495, RMSE: 0.5333867228536826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1024/1024 [00:02<00:00, 388.93it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.39100958475319203, AUC: 0.6463055764966465, ACC: 0.6086158814031998, RMSE: 0.535919654704651\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 273/273 [00:00<00:00, 700.06it/s]\n",
      "[I 2025-01-28 21:09:09,378] Trial 30 finished with value: 0.6463055764966465 and parameters: {'batch_size': 100, 'learning_rate': 0.038810054885776955}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000714\n",
      "Using device: cuda\n",
      "Seed: 1738069749\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 52\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1970/1970 [00:04<00:00, 470.39it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9702778975370572, AUC: 0.629415877777767, ACC: 0.5140907089387935, RMSE: 0.5047634903324223\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1970/1970 [00:04<00:00, 469.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6963133020146849, AUC: 0.6618538271959495, ACC: 0.5140907089387935, RMSE: 0.504872811576521\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1970/1970 [00:04<00:00, 466.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6957788768153506, AUC: 0.666807492134828, ACC: 0.5140907089387935, RMSE: 0.5042539852425142\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1970/1970 [00:04<00:00, 469.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.692331711047797, AUC: 0.6685563238806811, ACC: 0.5140907089387935, RMSE: 0.5032908615183729\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1970/1970 [00:04<00:00, 468.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6805458787734133, AUC: 0.6697956052492766, ACC: 0.5148246000293556, RMSE: 0.49936119572479376\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1970/1970 [00:04<00:00, 467.66it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.656391297031175, AUC: 0.6719070817110051, ACC: 0.5512255981212388, RMSE: 0.49225597785801944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1970/1970 [00:04<00:00, 467.29it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6295675632917337, AUC: 0.6755437703299723, ACC: 0.5925436665198884, RMSE: 0.48502925708939365\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1970/1970 [00:04<00:00, 397.18it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6106924533541432, AUC: 0.679842671766267, ACC: 0.616541905181271, RMSE: 0.4814597459230907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1970/1970 [00:06<00:00, 282.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5966067877517739, AUC: 0.6844106001916124, ACC: 0.6285777190664905, RMSE: 0.47826888904804593\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1970/1970 [00:07<00:00, 280.28it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5826600238907761, AUC: 0.6885244546125973, ACC: 0.633494789373257, RMSE: 0.4769306025649043\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 525/525 [00:00<00:00, 1003.82it/s]\n",
      "[I 2025-01-28 21:10:02,147] Trial 31 finished with value: 0.6885244546125973 and parameters: {'batch_size': 52, 'learning_rate': 0.0007139541300690893}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000926\n",
      "Using device: cuda\n",
      "Seed: 1738069802\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 48\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2134/2134 [00:07<00:00, 297.25it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9549120331938957, AUC: 0.6394314221582724, ACC: 0.5140907089387935, RMSE: 0.5041144844157259\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6976000066940876, AUC: 0.6644100094210653, ACC: 0.5140907089387935, RMSE: 0.5043475722782874\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.692626371700404, AUC: 0.6675514102436809, ACC: 0.5140907089387935, RMSE: 0.5025132334180039\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2134/2134 [00:06<00:00, 351.32it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6706858801445116, AUC: 0.6699697747557924, ACC: 0.5371348891824453, RMSE: 0.495665928649724\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2134/2134 [00:04<00:00, 471.55it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6357552239491544, AUC: 0.6747932330036769, ACC: 0.5905621605753706, RMSE: 0.4856074352588058\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6118793562264117, AUC: 0.6807894031287769, ACC: 0.6216791428152062, RMSE: 0.481034784351972\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2134/2134 [00:04<00:00, 470.95it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5946652167236682, AUC: 0.686575062734334, ACC: 0.6312197269925143, RMSE: 0.47785208537778673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2134/2134 [00:04<00:00, 471.22it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5771109709950843, AUC: 0.6912118568942438, ACC: 0.6343754586819316, RMSE: 0.47664351930951193\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2134/2134 [00:05<00:00, 383.80it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5568546399739488, AUC: 0.6942579474539783, ACC: 0.6385586378981359, RMSE: 0.4766392048220376\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5320149542800489, AUC: 0.6957148113399053, ACC: 0.6405401438426538, RMSE: 0.477903534094362\n"
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     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 569/569 [00:00<00:00, 1025.77it/s]\n",
      "[I 2025-01-28 21:11:09,686] Trial 32 finished with value: 0.6957148113399053 and parameters: {'batch_size': 48, 'learning_rate': 0.0009256371802081371}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001824\n",
      "Using device: cuda\n",
      "Seed: 1738069869\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 43\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2382/2382 [00:08<00:00, 278.24it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.792885832719999, AUC: 0.6620754523949439, ACC: 0.4859092910612065, RMSE: 0.501383656246483\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2382/2382 [00:09<00:00, 250.60it/s]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6706120951725193, AUC: 0.6707482766587095, ACC: 0.581168354616175, RMSE: 0.4853215974472619\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2382/2382 [00:09<00:00, 245.01it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6231224034685332, AUC: 0.6819782210497368, ACC: 0.6258623220314106, RMSE: 0.4792368862349617\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2382/2382 [00:09<00:00, 241.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5946102295474381, AUC: 0.6913796766954279, ACC: 0.6373844121532365, RMSE: 0.4766245203157661\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2382/2382 [00:09<00:00, 251.69it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5554300615459206, AUC: 0.6960095282233622, ACC: 0.6395126963158667, RMSE: 0.4791768327912907\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2382/2382 [00:09<00:00, 245.15it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5011145606086497, AUC: 0.6933434281789574, ACC: 0.6384852487890798, RMSE: 0.4898161645118745\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2382/2382 [00:09<00:00, 244.80it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.4405499139720907, AUC: 0.6846713693295865, ACC: 0.6321737854102452, RMSE: 0.5043871705033279\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2382/2382 [00:09<00:00, 246.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.393253085789162, AUC: 0.6760693944095211, ACC: 0.6278438279759284, RMSE: 0.5163025859428925\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2382/2382 [00:09<00:00, 246.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3616596816293739, AUC: 0.669541185816634, ACC: 0.6237340378687802, RMSE: 0.5245247241114197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2382/2382 [00:09<00:00, 248.15it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3434318690853996, AUC: 0.6647296723455025, ACC: 0.6235138705416117, RMSE: 0.5306872823912926\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 635/635 [00:00<00:00, 1098.40it/s]\n",
      "[I 2025-01-28 21:12:50,087] Trial 33 finished with value: 0.6647296723455025 and parameters: {'batch_size': 43, 'learning_rate': 0.0018238102736533634}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000280\n",
      "Using device: cuda\n",
      "Seed: 1738069970\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 63\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1626/1626 [00:03<00:00, 448.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.5825341691023602, AUC: 0.5464694829819812, ACC: 0.5140907089387935, RMSE: 0.501922849114037\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1626/1626 [00:03<00:00, 447.16it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6931419696608446, AUC: 0.5889888778820143, ACC: 0.5140907089387935, RMSE: 0.5013680010646395\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1626/1626 [00:03<00:00, 447.38it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6930801743804162, AUC: 0.6200617915806789, ACC: 0.5140907089387935, RMSE: 0.5013689162147228\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1626/1626 [00:03<00:00, 447.82it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6932177712629936, AUC: 0.6462502726115, ACC: 0.5140907089387935, RMSE: 0.5014785774219521\n"
     ]
    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6932511038592527, AUC: 0.6569958282759386, ACC: 0.5140907089387935, RMSE: 0.5014426343601385\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6930398846830975, AUC: 0.6617986203351631, ACC: 0.5140907089387935, RMSE: 0.5014825262428836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6929803094740723, AUC: 0.6643158311090499, ACC: 0.5140907089387935, RMSE: 0.5014810946205601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1626/1626 [00:03<00:00, 448.90it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6927012217220785, AUC: 0.6659422504541548, ACC: 0.5140907089387935, RMSE: 0.501461690150487\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1626/1626 [00:03<00:00, 446.01it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6923202381802661, AUC: 0.6670987484827815, ACC: 0.5140907089387935, RMSE: 0.5011794896203613\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1626/1626 [00:05<00:00, 307.11it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.691842585435506, AUC: 0.6680753956895958, ACC: 0.5140907089387935, RMSE: 0.5009484983520824\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 433/433 [00:00<00:00, 904.04it/s]\n",
      "[I 2025-01-28 21:13:32,294] Trial 34 finished with value: 0.6680753956895958 and parameters: {'batch_size': 63, 'learning_rate': 0.00027960582839629415}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001071\n",
      "Using device: cuda\n",
      "Seed: 1738070012\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 49\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.070391213266473, AUC: 0.6431630545036497, ACC: 0.5140907089387935, RMSE: 0.5035189998550592\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6976198480745252, AUC: 0.6641770431524465, ACC: 0.5140907089387935, RMSE: 0.5044307992706486\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6861908813127491, AUC: 0.6678673539009884, ACC: 0.5149713782474681, RMSE: 0.4996111015915033\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6502958387992028, AUC: 0.6724229925740789, ACC: 0.5692059298400117, RMSE: 0.48929097441376096\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6190799753774296, AUC: 0.678882421676277, ACC: 0.6180096873623954, RMSE: 0.4820761639483795\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5989161398137015, AUC: 0.6855470465191918, ACC: 0.6299721121385586, RMSE: 0.47960647754802666\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5795804625065133, AUC: 0.6906674747717798, ACC: 0.6346690151181564, RMSE: 0.4778107903446842\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5559990500148974, AUC: 0.6943104052912341, ACC: 0.6385586378981359, RMSE: 0.47735130148793636\n"
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    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch: 8, Loss: 0.5266705059691479, AUC: 0.6955094107699842, ACC: 0.6387788052253046, RMSE: 0.4802406830483908\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 557/557 [00:00<00:00, 1014.57it/s]\n",
      "[I 2025-01-28 21:14:39,706] Trial 35 finished with value: 0.6944035917986817 and parameters: {'batch_size': 49, 'learning_rate': 0.0010712520591179939}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002860\n",
      "Using device: cuda\n",
      "Seed: 1738070079\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 48\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2134/2134 [00:04<00:00, 475.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8501222554974838, AUC: 0.6650801243334831, ACC: 0.5201820049904594, RMSE: 0.4967639317275471\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2134/2134 [00:04<00:00, 473.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6403022470911083, AUC: 0.6805512622276297, ACC: 0.6232203141053868, RMSE: 0.47975034602953937\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2134/2134 [00:08<00:00, 247.94it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.59858871282767, AUC: 0.6916716014334164, ACC: 0.6378247468075737, RMSE: 0.47886312686418175\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2134/2134 [00:08<00:00, 245.02it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5373801455781521, AUC: 0.6934673821889794, ACC: 0.637237633935124, RMSE: 0.48692048424560347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2134/2134 [00:08<00:00, 244.79it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.45106259408042937, AUC: 0.6810038161836848, ACC: 0.6274768824306473, RMSE: 0.5073849245341626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2134/2134 [00:08<00:00, 255.13it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3883482950941189, AUC: 0.6704999266388034, ACC: 0.6227065903419933, RMSE: 0.5212240296910311\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2134/2134 [00:08<00:00, 254.91it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3557440232914971, AUC: 0.6641560104273156, ACC: 0.6208718626155878, RMSE: 0.5297835079182645\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2134/2134 [00:08<00:00, 244.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.33934216226617814, AUC: 0.6593156915017765, ACC: 0.6193306913254073, RMSE: 0.536358683332347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2134/2134 [00:08<00:00, 254.47it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 284/284 [00:00<00:00, 1005.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.33066296466340805, AUC: 0.655201653812556, ACC: 0.6146337883458095, RMSE: 0.5380717215598968\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 569/569 [00:00<00:00, 1042.54it/s]\n",
      "[I 2025-01-28 21:15:52,866] Trial 36 finished with value: 0.655201653812556 and parameters: {'batch_size': 48, 'learning_rate': 0.002859928986982395}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000399\n",
      "Using device: cuda\n",
      "Seed: 1738070152\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 19\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 5390/5390 [00:14<00:00, 361.88it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0840358135755241, AUC: 0.629436619429818, ACC: 0.5140907089387935, RMSE: 0.49981991517809726\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 5390/5390 [00:21<00:00, 255.78it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6961524378833169, AUC: 0.6605886834451971, ACC: 0.5140907089387935, RMSE: 0.49978621283329705\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 5390/5390 [00:21<00:00, 255.97it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6955449642133182, AUC: 0.6650193655232131, ACC: 0.5140907089387935, RMSE: 0.4997468050631425\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 5390/5390 [00:21<00:00, 255.60it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.69325616963498, AUC: 0.6668104998899851, ACC: 0.5140907089387935, RMSE: 0.4991978219932353\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 5390/5390 [00:22<00:00, 241.21it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6862753948165667, AUC: 0.6677697473949229, ACC: 0.5168061059738734, RMSE: 0.4968228507330046\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 5390/5390 [00:22<00:00, 236.71it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.670110223316527, AUC: 0.668889020410799, ACC: 0.5522530456480258, RMSE: 0.4914718997291946\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 5390/5390 [00:19<00:00, 280.25it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6471170674947285, AUC: 0.6709415491836425, ACC: 0.5852047556142669, RMSE: 0.4850782487330534\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 5390/5390 [00:19<00:00, 283.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6265383598811108, AUC: 0.6739718463336812, ACC: 0.6152942903273154, RMSE: 0.48061548160363265\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 5390/5390 [00:19<00:00, 270.03it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6129242309059858, AUC: 0.6774624809495364, ACC: 0.6242477616321738, RMSE: 0.4780971676933224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 5390/5390 [00:12<00:00, 427.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6021958602409194, AUC: 0.680977792094261, ACC: 0.6282841626302657, RMSE: 0.476006331720776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 1436/1436 [00:00<00:00, 1439.29it/s]\n",
      "[I 2025-01-28 21:19:14,369] Trial 37 finished with value: 0.680977792094261 and parameters: {'batch_size': 19, 'learning_rate': 0.00039907728331262115}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000065\n",
      "Using device: cuda\n",
      "Seed: 1738070354\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 37\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2768/2768 [00:10<00:00, 251.84it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 4.476416386839546, AUC: 0.49729290177329266, ACC: 0.5140907089387935, RMSE: 0.5267000831413495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2768/2768 [00:10<00:00, 254.63it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.705253699374509, AUC: 0.5022343739842763, ACC: 0.5140907089387935, RMSE: 0.5018003958458778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2768/2768 [00:10<00:00, 257.27it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6930053262955191, AUC: 0.5218304055154683, ACC: 0.5140907089387935, RMSE: 0.5007479565649317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2768/2768 [00:10<00:00, 260.57it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6927136337180013, AUC: 0.5593759328487937, ACC: 0.5140907089387935, RMSE: 0.5006123947434292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2768/2768 [00:10<00:00, 261.59it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6924001654190135, AUC: 0.5942344459979123, ACC: 0.5140907089387935, RMSE: 0.5005990210904957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2768/2768 [00:10<00:00, 259.47it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6923199901088125, AUC: 0.6168835861842055, ACC: 0.5140907089387935, RMSE: 0.5006572157055944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2768/2768 [00:09<00:00, 294.61it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6923616815982871, AUC: 0.6310274523958064, ACC: 0.5140907089387935, RMSE: 0.5006541680340677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2768/2768 [00:10<00:00, 273.62it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6922268079798346, AUC: 0.6402455104403063, ACC: 0.5140907089387935, RMSE: 0.5005787502375459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2768/2768 [00:09<00:00, 279.29it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6921435445116434, AUC: 0.6467699027417037, ACC: 0.5140907089387935, RMSE: 0.5006331357084001\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2768/2768 [00:07<00:00, 373.18it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6922203072621299, AUC: 0.6513172727832333, ACC: 0.5140907089387935, RMSE: 0.5005732292328802\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 738/738 [00:00<00:00, 1162.28it/s]\n",
      "[I 2025-01-28 21:21:00,820] Trial 38 finished with value: 0.6513172727832333 and parameters: {'batch_size': 37, 'learning_rate': 6.536521965680153e-05}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001381\n",
      "Using device: cuda\n",
      "Seed: 1738070460\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 57\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1797/1797 [00:06<00:00, 287.26it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9499155806712329, AUC: 0.6532046337540633, ACC: 0.5140907089387935, RMSE: 0.5032386516493194\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1797/1797 [00:07<00:00, 240.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6963559258428625, AUC: 0.6671888302107121, ACC: 0.5140907089387935, RMSE: 0.5047782300739435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1797/1797 [00:07<00:00, 240.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6663963175775213, AUC: 0.6715284064147764, ACC: 0.5582709525906355, RMSE: 0.4931129144083426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1797/1797 [00:07<00:00, 243.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.624822858727237, AUC: 0.6791509851044968, ACC: 0.6155878467635403, RMSE: 0.48337194115943144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1797/1797 [00:07<00:00, 242.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6010464045592793, AUC: 0.6867241460535718, ACC: 0.6309261705562894, RMSE: 0.47943853484327975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1797/1797 [00:07<00:00, 243.32it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5771256579779623, AUC: 0.692635074888252, ACC: 0.6378247468075737, RMSE: 0.47820378187789353\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1797/1797 [00:07<00:00, 239.07it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5470852208555441, AUC: 0.6959677969681182, ACC: 0.6386320270071921, RMSE: 0.4783618649071488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1797/1797 [00:07<00:00, 241.60it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5087866453110806, AUC: 0.6957772087838094, ACC: 0.6367239101717305, RMSE: 0.4832325104574639\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1797/1797 [00:07<00:00, 246.22it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 240/240 [00:00<00:00, 930.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.4662819709722108, AUC: 0.6920227110309475, ACC: 0.6380449141347424, RMSE: 0.4907334961167614\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1797/1797 [00:07<00:00, 244.79it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 240/240 [00:00<00:00, 930.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.4259537426162973, AUC: 0.6860258509548437, ACC: 0.6326141200645824, RMSE: 0.5016487252661713\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 479/479 [00:00<00:00, 941.63it/s]\n",
      "[I 2025-01-28 21:22:18,172] Trial 39 finished with value: 0.6860258509548437 and parameters: {'batch_size': 57, 'learning_rate': 0.0013805910412094096}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.005470\n",
      "Using device: cuda\n",
      "Seed: 1738070538\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 49\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2090/2090 [00:07<00:00, 297.71it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 279/279 [00:00<00:00, 1023.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7259924752575359, AUC: 0.6763214960380102, ACC: 0.6196242477616322, RMSE: 0.4790744056944088\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2090/2090 [00:07<00:00, 264.82it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6116805198517713, AUC: 0.6921176655378423, ACC: 0.6385586378981359, RMSE: 0.47796321338427916\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2090/2090 [00:08<00:00, 253.95it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.5059772183806702, AUC: 0.6800317506827551, ACC: 0.6271099368853662, RMSE: 0.510406297754561\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2090/2090 [00:08<00:00, 250.24it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.40236164244453304, AUC: 0.6669398333617399, ACC: 0.617862909144283, RMSE: 0.5233580603323893\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2090/2090 [00:07<00:00, 283.56it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.36177345220908597, AUC: 0.661488940139312, ACC: 0.6202847497431381, RMSE: 0.5312335608364125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2090/2090 [00:08<00:00, 245.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.34961068097626763, AUC: 0.6551230209590944, ACC: 0.6194774695435198, RMSE: 0.5367605668591204\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2090/2090 [00:08<00:00, 241.27it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.34267921159890563, AUC: 0.6534081693001772, ACC: 0.6097901071480992, RMSE: 0.5387341371533888\n",
      "[NCDM] Early stopping at epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 557/557 [00:00<00:00, 1015.72it/s]\n",
      "[I 2025-01-28 21:23:17,899] Trial 40 finished with value: 0.6534081693001772 and parameters: {'batch_size': 49, 'learning_rate': 0.0054700821161526025}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001031\n",
      "Using device: cuda\n",
      "Seed: 1738070597\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 68\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1506/1506 [00:04<00:00, 365.04it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9974896233593167, AUC: 0.6269887271708419, ACC: 0.5140907089387935, RMSE: 0.5055587985996012\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1506/1506 [00:05<00:00, 259.60it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6968697586738731, AUC: 0.659723172252413, ACC: 0.5140907089387935, RMSE: 0.5053147430128382\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1506/1506 [00:06<00:00, 242.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6937898083591207, AUC: 0.6656004336342058, ACC: 0.5140907089387935, RMSE: 0.5048673399667368\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1506/1506 [00:06<00:00, 237.91it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.678469602508374, AUC: 0.6683100760552396, ACC: 0.5203287832085719, RMSE: 0.4990797491996552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1506/1506 [00:06<00:00, 243.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6441494658450839, AUC: 0.6727535761292477, ACC: 0.5691325407309555, RMSE: 0.48915975442085735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1506/1506 [00:06<00:00, 245.52it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6161851390384899, AUC: 0.6789023547919953, ACC: 0.6138998972552473, RMSE: 0.4820869023199648\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1506/1506 [00:06<00:00, 244.57it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5972421094676255, AUC: 0.684906761207203, ACC: 0.6306326141200645, RMSE: 0.4785120558686519\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1506/1506 [00:06<00:00, 243.70it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 201/201 [00:00<00:00, 852.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5800524055364122, AUC: 0.6897041199022726, ACC: 0.6370174666079553, RMSE: 0.47637218243975965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1506/1506 [00:06<00:00, 244.38it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 201/201 [00:00<00:00, 851.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5598950353197526, AUC: 0.6931751987193647, ACC: 0.6396594745339792, RMSE: 0.4761062628476177\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1506/1506 [00:06<00:00, 246.31it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 201/201 [00:00<00:00, 852.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5357997662240607, AUC: 0.6951092068463407, ACC: 0.6395860854249229, RMSE: 0.4769786373314227\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 402/402 [00:00<00:00, 874.59it/s]\n",
      "[I 2025-01-28 21:24:21,483] Trial 41 finished with value: 0.6951092068463407 and parameters: {'batch_size': 68, 'learning_rate': 0.0010313585130921087}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000499\n",
      "Using device: cuda\n",
      "Seed: 1738070661\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 58\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1766/1766 [00:04<00:00, 357.99it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.3484627832811524, AUC: 0.588797330234591, ACC: 0.5140907089387935, RMSE: 0.5022822459706551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6945192855387284, AUC: 0.6338864153067355, ACC: 0.5140907089387935, RMSE: 0.5034859876443243\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1766/1766 [00:05<00:00, 307.30it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6947614398372538, AUC: 0.6581690899578602, ACC: 0.5140907089387935, RMSE: 0.5033474079680772\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1766/1766 [00:07<00:00, 247.41it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6945081580670747, AUC: 0.6643193994493974, ACC: 0.5140907089387935, RMSE: 0.503064695498179\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1766/1766 [00:07<00:00, 246.35it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6938709723989934, AUC: 0.6666963022183758, ACC: 0.5140907089387935, RMSE: 0.5033136883410455\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1766/1766 [00:07<00:00, 246.53it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6920743656522961, AUC: 0.6680385048718627, ACC: 0.5140907089387935, RMSE: 0.5025748745223653\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1766/1766 [00:07<00:00, 246.45it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.687709445726561, AUC: 0.6689468792707562, ACC: 0.5140907089387935, RMSE: 0.5012927736713243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1766/1766 [00:07<00:00, 246.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6787566232451766, AUC: 0.6698086172939884, ACC: 0.5141640980478497, RMSE: 0.49883676685805245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1766/1766 [00:07<00:00, 246.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6637929827312381, AUC: 0.6709958828251898, ACC: 0.5340525466020842, RMSE: 0.4949092471879115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1766/1766 [00:07<00:00, 246.14it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6440856639181906, AUC: 0.6727634079310515, ACC: 0.564875972405695, RMSE: 0.4886801168070123\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 471/471 [00:00<00:00, 949.89it/s]\n",
      "[I 2025-01-28 21:25:33,348] Trial 42 finished with value: 0.6727634079310515 and parameters: {'batch_size': 58, 'learning_rate': 0.0004991566229275577}. Best is trial 7 with value: 0.6959082779135579.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001166\n",
      "Using device: cuda\n",
      "Seed: 1738070733\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 81\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1265/1265 [00:05<00:00, 239.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0390948489956233, AUC: 0.6340297202863168, ACC: 0.5140907089387935, RMSE: 0.5069468501856519\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1265/1265 [00:05<00:00, 236.77it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6969062833446759, AUC: 0.6627817250521533, ACC: 0.5140907089387935, RMSE: 0.5070710307018552\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1265/1265 [00:05<00:00, 237.96it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6931498119011227, AUC: 0.6673407164559029, ACC: 0.5140907089387935, RMSE: 0.5059065378887595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1265/1265 [00:05<00:00, 236.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6753575026046618, AUC: 0.6698839189777599, ACC: 0.5245853515338323, RMSE: 0.4990699610019545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1265/1265 [00:05<00:00, 241.99it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6388589986934964, AUC: 0.6747313745839947, ACC: 0.5849845882870982, RMSE: 0.48685907884652446\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1265/1265 [00:05<00:00, 238.70it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6112445263287767, AUC: 0.6811255494139136, ACC: 0.622926757669162, RMSE: 0.4808402522060351\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1265/1265 [00:05<00:00, 239.14it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5913097991538142, AUC: 0.6870893349637738, ACC: 0.6323939527374137, RMSE: 0.47741799203118834\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1265/1265 [00:05<00:00, 240.86it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5717474088602857, AUC: 0.6918002665151362, ACC: 0.636870688389843, RMSE: 0.47635715439016957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1265/1265 [00:05<00:00, 236.20it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5485051926888025, AUC: 0.6947774158769152, ACC: 0.6386320270071921, RMSE: 0.4763779336336019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1265/1265 [00:05<00:00, 236.01it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.521162881858264, AUC: 0.6959084719622778, ACC: 0.6396594745339792, RMSE: 0.4787491998874821\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 337/337 [00:00<00:00, 791.82it/s]\n",
      "[I 2025-01-28 21:26:30,275] Trial 43 finished with value: 0.6959084719622778 and parameters: {'batch_size': 81, 'learning_rate': 0.0011662422916386289}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002118\n",
      "Using device: cuda\n",
      "Seed: 1738070790\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 89\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1151/1151 [00:03<00:00, 325.59it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8696910024569616, AUC: 0.6499593521834415, ACC: 0.5140907089387935, RMSE: 0.5077982631307474\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6912260472929861, AUC: 0.6659826449293291, ACC: 0.5147512109202994, RMSE: 0.5026848383344172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1151/1151 [00:04<00:00, 245.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6427933908772406, AUC: 0.6753051443070256, ACC: 0.5979010714809923, RMSE: 0.48565942630572434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.60516620759442, AUC: 0.6864666110609281, ACC: 0.6309995596653457, RMSE: 0.47906852378065434\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5723441103093009, AUC: 0.6934939884245627, ACC: 0.6389989725524732, RMSE: 0.47826639036451035\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1151/1151 [00:04<00:00, 240.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5258738653075685, AUC: 0.6952283096383676, ACC: 0.6367972992807868, RMSE: 0.4818895362973331\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1151/1151 [00:04<00:00, 238.71it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.4713413844825287, AUC: 0.6911425922817553, ACC: 0.6373844121532365, RMSE: 0.4907860847532936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1151/1151 [00:04<00:00, 243.23it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.41905136078880934, AUC: 0.6835677172356552, ACC: 0.6321003963011889, RMSE: 0.5033340747667652\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1151/1151 [00:04<00:00, 245.72it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.38078065852203336, AUC: 0.676313540040498, ACC: 0.6244679289593424, RMSE: 0.5147229480843605\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1151/1151 [00:04<00:00, 246.02it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3551067864242375, AUC: 0.6708485998468524, ACC: 0.6221194774695435, RMSE: 0.5231648346891221\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 307/307 [00:00<00:00, 753.05it/s]\n",
      "[I 2025-01-28 21:27:20,083] Trial 44 finished with value: 0.6708485998468524 and parameters: {'batch_size': 89, 'learning_rate': 0.0021175963761852438}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000317\n",
      "Using device: cuda\n",
      "Seed: 1738070840\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 79\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1297/1297 [00:05<00:00, 249.47it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.1285544878809657, AUC: 0.5391899500874351, ACC: 0.5140907089387935, RMSE: 0.5026785995316316\n"
     ]
    },
    {
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     "text": [
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6933549410945742, AUC: 0.5850991497324122, ACC: 0.5140907089387935, RMSE: 0.5012038568695819\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch: 2, Loss: 0.6929672475769231, AUC: 0.6186924760088405, ACC: 0.5140907089387935, RMSE: 0.5012182650945746\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6929872855831314, AUC: 0.6445962444457597, ACC: 0.5140907089387935, RMSE: 0.5012364013788609\n"
     ]
    },
    {
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    },
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6929442079690391, AUC: 0.6579346683238427, ACC: 0.5140907089387935, RMSE: 0.5013068214887031\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1297/1297 [00:05<00:00, 245.25it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6929524171876282, AUC: 0.6635145716034925, ACC: 0.5140907089387935, RMSE: 0.5013644431775067\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1297/1297 [00:05<00:00, 245.27it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6928592319201762, AUC: 0.6661323642971485, ACC: 0.5140907089387935, RMSE: 0.5013222160409097\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1297/1297 [00:05<00:00, 236.32it/s]\n",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6925994911925464, AUC: 0.6677564766185846, ACC: 0.5140907089387935, RMSE: 0.5013747316620857\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1297/1297 [00:05<00:00, 245.12it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6923031095090791, AUC: 0.6688140852634982, ACC: 0.5140907089387935, RMSE: 0.501269315857472\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1297/1297 [00:05<00:00, 245.20it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6917197773248853, AUC: 0.6696685680207064, ACC: 0.5140907089387935, RMSE: 0.5011553635399125\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 346/346 [00:00<00:00, 812.90it/s]\n",
      "[I 2025-01-28 21:28:17,013] Trial 45 finished with value: 0.6696685680207064 and parameters: {'batch_size': 79, 'learning_rate': 0.0003173615378307067}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001127\n",
      "Using device: cuda\n",
      "Seed: 1738070897\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 70\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1463/1463 [00:05<00:00, 279.19it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0842683545058505, AUC: 0.6423697617760891, ACC: 0.5140907089387935, RMSE: 0.5065445937351902\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1463/1463 [00:06<00:00, 238.17it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6974478099375495, AUC: 0.6639990573544411, ACC: 0.5140907089387935, RMSE: 0.5063519174410267\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1463/1463 [00:06<00:00, 235.92it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6925841741421739, AUC: 0.6674480146174745, ACC: 0.5140907089387935, RMSE: 0.5041533642431106\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1463/1463 [00:05<00:00, 256.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6704026081752712, AUC: 0.670089890913356, ACC: 0.5386026713635697, RMSE: 0.495731226029541\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1463/1463 [00:03<00:00, 396.96it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6332044074246921, AUC: 0.6754012738867237, ACC: 0.5918831645383825, RMSE: 0.485961168687972\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1463/1463 [00:03<00:00, 373.93it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6078927148227731, AUC: 0.6819709873446815, ACC: 0.6256421547042419, RMSE: 0.47989032232890033\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1463/1463 [00:06<00:00, 241.10it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5897666018300894, AUC: 0.6877596654858802, ACC: 0.6329076765008073, RMSE: 0.4773359232150435\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1463/1463 [00:06<00:00, 238.32it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.569636734458411, AUC: 0.6920732391614897, ACC: 0.6357698517539997, RMSE: 0.47724773087856287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1463/1463 [00:06<00:00, 235.70it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5444800068031658, AUC: 0.6948238043014349, ACC: 0.6385586378981359, RMSE: 0.47758478265021426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1463/1463 [00:06<00:00, 238.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5170021045110198, AUC: 0.6957043650504887, ACC: 0.638925583443417, RMSE: 0.47950654281249644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 390/390 [00:00<00:00, 859.28it/s]\n",
      "[I 2025-01-28 21:29:16,502] Trial 46 finished with value: 0.6957043650504887 and parameters: {'batch_size': 70, 'learning_rate': 0.0011274346090416863}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.003279\n",
      "Using device: cuda\n",
      "Seed: 1738070956\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 93\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1102/1102 [00:03<00:00, 304.66it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8030991874729873, AUC: 0.6610940617749788, ACC: 0.5140907089387935, RMSE: 0.5023336800964147\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1102/1102 [00:04<00:00, 243.56it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6578363310511878, AUC: 0.6753055970873719, ACC: 0.5981212388081608, RMSE: 0.48895774027945227\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1102/1102 [00:04<00:00, 274.13it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.606652482262324, AUC: 0.6889366787763849, ACC: 0.6310729487744019, RMSE: 0.48110624071620955\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1102/1102 [00:02<00:00, 369.13it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5551263122750064, AUC: 0.6940521587866177, ACC: 0.6358432408630559, RMSE: 0.4854382528131666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1102/1102 [00:03<00:00, 286.91it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.47727331515070315, AUC: 0.6869987357725904, ACC: 0.6327608982826949, RMSE: 0.49906294929793027\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1102/1102 [00:04<00:00, 243.60it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.40600291747065725, AUC: 0.6763515628090967, ACC: 0.625348598268017, RMSE: 0.5136350741822777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1102/1102 [00:04<00:00, 243.29it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3678235175686393, AUC: 0.6700893087671966, ACC: 0.6163951269631587, RMSE: 0.5261008596552469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1102/1102 [00:04<00:00, 243.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.35204952049277005, AUC: 0.6651677912329004, ACC: 0.6194040804344635, RMSE: 0.5307586675659854\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1102/1102 [00:04<00:00, 243.51it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.34328699083711456, AUC: 0.6620948464864407, ACC: 0.6213855863789813, RMSE: 0.538124131633254\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 294/294 [00:00<00:00, 734.37it/s]\n",
      "[I 2025-01-28 21:29:57,023] Trial 47 finished with value: 0.6620948464864407 and parameters: {'batch_size': 93, 'learning_rate': 0.0032793728953161507}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000597\n",
      "Using device: cuda\n",
      "Seed: 1738070997\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 97\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1056/1056 [00:04<00:00, 258.73it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.5696481510601712, AUC: 0.5859546997575793, ACC: 0.5140907089387935, RMSE: 0.5012899966293514\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6936507375177109, AUC: 0.6219379731891508, ACC: 0.5140907089387935, RMSE: 0.5015941550024099\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6936779922495285, AUC: 0.6504386525213774, ACC: 0.5140907089387935, RMSE: 0.5017090379334047\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.693764740036744, AUC: 0.661490384724226, ACC: 0.5140907089387935, RMSE: 0.5016574929644765\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6932344176439624, AUC: 0.6653134571385727, ACC: 0.5140907089387935, RMSE: 0.5016716047522877\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1056/1056 [00:04<00:00, 241.42it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6922938893125816, AUC: 0.667346160600542, ACC: 0.5140907089387935, RMSE: 0.5012251595265772\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1056/1056 [00:04<00:00, 241.59it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6899207500232891, AUC: 0.668650114095257, ACC: 0.5140907089387935, RMSE: 0.5009020645546849\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1056/1056 [00:04<00:00, 242.07it/s]\n",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6850739552667646, AUC: 0.6696334667633891, ACC: 0.5140907089387935, RMSE: 0.4996191393949377\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1056/1056 [00:04<00:00, 240.50it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6759167031356783, AUC: 0.6705458407220078, ACC: 0.5159254366651989, RMSE: 0.49699362553213366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1056/1056 [00:04<00:00, 241.85it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6604565781061397, AUC: 0.6719342808732323, ACC: 0.5413180683986496, RMSE: 0.49264969136908093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 282/282 [00:00<00:00, 704.11it/s]\n",
      "[I 2025-01-28 21:30:44,283] Trial 48 finished with value: 0.6719342808732323 and parameters: {'batch_size': 97, 'learning_rate': 0.0005969009425327903}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000151\n",
      "Using device: cuda\n",
      "Seed: 1738071044\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 83\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1234/1234 [00:04<00:00, 288.72it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.6108381749407013, AUC: 0.5245185516505408, ACC: 0.5140907089387935, RMSE: 0.5265686370285201\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1234/1234 [00:05<00:00, 240.07it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.7070955458780934, AUC: 0.5294927210708126, ACC: 0.5140907089387935, RMSE: 0.5020566084617786\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1234/1234 [00:05<00:00, 240.14it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6934992265276236, AUC: 0.5399651423816311, ACC: 0.5140907089387935, RMSE: 0.5006010313017712\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1234/1234 [00:05<00:00, 241.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6926537971925504, AUC: 0.5606630472268228, ACC: 0.5140907089387935, RMSE: 0.5005299447226529\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1234/1234 [00:05<00:00, 240.22it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6925510904197755, AUC: 0.5923613907299261, ACC: 0.5140907089387935, RMSE: 0.5005988355557389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1234/1234 [00:05<00:00, 240.32it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6924628773809832, AUC: 0.6222326146092165, ACC: 0.5140907089387935, RMSE: 0.5006165610106528\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1234/1234 [00:05<00:00, 240.23it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6925287589648168, AUC: 0.6419448813235762, ACC: 0.5140907089387935, RMSE: 0.5005637209450543\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1234/1234 [00:05<00:00, 237.09it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6923190266425258, AUC: 0.6532755801221235, ACC: 0.5140907089387935, RMSE: 0.50063325260136\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1234/1234 [00:05<00:00, 240.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6923987879938592, AUC: 0.6594944858361145, ACC: 0.5140907089387935, RMSE: 0.5006658193796575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1234/1234 [00:05<00:00, 240.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6923784782666248, AUC: 0.6631395940134245, ACC: 0.5140907089387935, RMSE: 0.5005840274473176\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 329/329 [00:00<00:00, 767.17it/s]\n",
      "[I 2025-01-28 21:31:38,748] Trial 49 finished with value: 0.6631395940134245 and parameters: {'batch_size': 83, 'learning_rate': 0.00015110856045030872}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.004359\n",
      "Using device: cuda\n",
      "Seed: 1738071098\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 125\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 820/820 [00:03<00:00, 239.16it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9515612930786319, AUC: 0.6598283035366134, ACC: 0.5140907089387935, RMSE: 0.5069226200978394\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6525222220435375, AUC: 0.6777388063265489, ACC: 0.6126522824012917, RMSE: 0.4859210345877271\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.5956488816839892, AUC: 0.6922744030010282, ACC: 0.6370908557170116, RMSE: 0.47983025444187755\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5199319567985651, AUC: 0.6908167737007064, ACC: 0.6318802289740203, RMSE: 0.48865065105025546\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.42850142110411715, AUC: 0.6785077890617108, ACC: 0.6274034933215911, RMSE: 0.5094616470756643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3799615703795741, AUC: 0.6721745131883595, ACC: 0.6257155438132981, RMSE: 0.5257954591987037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 820/820 [00:03<00:00, 230.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3583855187202372, AUC: 0.6676039758857812, ACC: 0.6225598121238808, RMSE: 0.5310539388867258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 820/820 [00:03<00:00, 230.57it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3474659445809155, AUC: 0.6624390134519962, ACC: 0.60802876853075, RMSE: 0.5385813362150732\n",
      "[NCDM] Early stopping at epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 219/219 [00:00<00:00, 576.16it/s]\n",
      "[I 2025-01-28 21:32:10,254] Trial 50 finished with value: 0.6624390134519962 and parameters: {'batch_size': 125, 'learning_rate': 0.0043585659291302155}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001183\n",
      "Using device: cuda\n",
      "Seed: 1738071130\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 69\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9237435747075964, AUC: 0.638060543416191, ACC: 0.5140907089387935, RMSE: 0.5087304393234401\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6975041560653082, AUC: 0.6641568620855861, ACC: 0.5140907089387935, RMSE: 0.5075396377231194\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1485/1485 [00:06<00:00, 235.72it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6898549879440153, AUC: 0.6680216765356611, ACC: 0.5140907089387935, RMSE: 0.5041487839645967\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6611453993151887, AUC: 0.6714917312067318, ACC: 0.5523998238661383, RMSE: 0.49414165298088025\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1485/1485 [00:06<00:00, 236.65it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6245520757605331, AUC: 0.6776274870442833, ACC: 0.6060472625862322, RMSE: 0.4838724250665683\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1485/1485 [00:06<00:00, 235.54it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6020381865678011, AUC: 0.684286193401244, ACC: 0.6278438279759284, RMSE: 0.4799214290371805\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1485/1485 [00:06<00:00, 236.79it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5826882546497917, AUC: 0.6897389624279635, ACC: 0.6332012329370321, RMSE: 0.4781752363396433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1485/1485 [00:06<00:00, 236.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5599892989051863, AUC: 0.6936976641169743, ACC: 0.6388521943343608, RMSE: 0.4771772140550317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1485/1485 [00:06<00:00, 236.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5321284457489296, AUC: 0.6955759910418486, ACC: 0.6401731982973726, RMSE: 0.478110441327072\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1485/1485 [00:06<00:00, 238.48it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5007427179728092, AUC: 0.695348889356762, ACC: 0.6388521943343608, RMSE: 0.48290584325348757\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 396/396 [00:00<00:00, 863.33it/s]\n",
      "[I 2025-01-28 21:33:15,644] Trial 51 finished with value: 0.695348889356762 and parameters: {'batch_size': 69, 'learning_rate': 0.0011828993277563427}. Best is trial 43 with value: 0.6959084719622778.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001179\n",
      "Using device: cuda\n",
      "Seed: 1738071195\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 70\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1463/1463 [00:05<00:00, 268.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0971586508695508, AUC: 0.6427138209368004, ACC: 0.5140907089387935, RMSE: 0.506802342609169\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1463/1463 [00:06<00:00, 240.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6973790298849446, AUC: 0.6657835832842551, ACC: 0.5140907089387935, RMSE: 0.5057418461910436\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1463/1463 [00:06<00:00, 242.82it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6909589839772011, AUC: 0.6686852908159652, ACC: 0.5140907089387935, RMSE: 0.5030823203596886\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1463/1463 [00:06<00:00, 242.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.663535872490864, AUC: 0.671774859069422, ACC: 0.5488771466314399, RMSE: 0.4945349491364524\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1463/1463 [00:06<00:00, 242.23it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6262434822439088, AUC: 0.6779838359572494, ACC: 0.6051665932775576, RMSE: 0.483737627861268\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1463/1463 [00:06<00:00, 243.06it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6035145036748885, AUC: 0.6845626050221317, ACC: 0.6281373844121533, RMSE: 0.47882814683217123\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1463/1463 [00:06<00:00, 241.70it/s]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5838740369431058, AUC: 0.6902182412049305, ACC: 0.6335681784823132, RMSE: 0.47704239441819496\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1463/1463 [00:06<00:00, 234.08it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5624788740849707, AUC: 0.6941973719119439, ACC: 0.6407603111698223, RMSE: 0.47610848430337094\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1463/1463 [00:06<00:00, 228.60it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5348681733979808, AUC: 0.6963050967650893, ACC: 0.6414208131513284, RMSE: 0.47702369897985214\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1463/1463 [00:06<00:00, 227.43it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.504512101371078, AUC: 0.696188915484344, ACC: 0.6400998091883164, RMSE: 0.48104794235845216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 390/390 [00:00<00:00, 845.09it/s]\n",
      "[I 2025-01-28 21:34:20,522] Trial 52 finished with value: 0.696188915484344 and parameters: {'batch_size': 70, 'learning_rate': 0.0011790680293471594}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000363\n",
      "Using device: cuda\n",
      "Seed: 1738071260\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 77\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1330/1330 [00:04<00:00, 284.71it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.7502579383608095, AUC: 0.563390261837484, ACC: 0.5140907089387935, RMSE: 0.5015508533302498\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1330/1330 [00:05<00:00, 238.15it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.693109272073086, AUC: 0.6017326071167799, ACC: 0.5140907089387935, RMSE: 0.5011660894896296\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1330/1330 [00:05<00:00, 239.29it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6930096516483708, AUC: 0.6301421805750547, ACC: 0.5140907089387935, RMSE: 0.5013752727518539\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1330/1330 [00:05<00:00, 238.40it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6930985486597047, AUC: 0.6531027366151931, ACC: 0.5140907089387935, RMSE: 0.5015227741547985\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6930823636234255, AUC: 0.6632626532432386, ACC: 0.5140907089387935, RMSE: 0.5014670999357017\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1330/1330 [00:05<00:00, 238.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6931207085462441, AUC: 0.6667523822984014, ACC: 0.5140907089387935, RMSE: 0.5013657135765154\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1330/1330 [00:04<00:00, 291.20it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6928445171144672, AUC: 0.6683612402343635, ACC: 0.5140907089387935, RMSE: 0.5012440415203159\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1330/1330 [00:03<00:00, 387.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6924309994045057, AUC: 0.6694381028244761, ACC: 0.5140907089387935, RMSE: 0.5012856311438328\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1330/1330 [00:03<00:00, 387.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6916975148190233, AUC: 0.6702685451013963, ACC: 0.5140907089387935, RMSE: 0.5009898743064637\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1330/1330 [00:05<00:00, 264.39it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6907090563971298, AUC: 0.6708358788752204, ACC: 0.5140907089387935, RMSE: 0.5007545614444311\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 355/355 [00:00<00:00, 816.07it/s]\n",
      "[I 2025-01-28 21:35:13,436] Trial 53 finished with value: 0.6708358788752204 and parameters: {'batch_size': 77, 'learning_rate': 0.00036345863616656457}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002494\n",
      "Using device: cuda\n",
      "Seed: 1738071313\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 72\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.829774608954693, AUC: 0.6598739804491603, ACC: 0.5140907089387935, RMSE: 0.5001803642555943\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6677314666108355, AUC: 0.6717887012114354, ACC: 0.5692793189490679, RMSE: 0.49225481039142027\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1423/1423 [00:05<00:00, 240.25it/s]\n",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6165612174940209, AUC: 0.6848594887829599, ACC: 0.6259357111404668, RMSE: 0.4819507115481893\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5808903378285912, AUC: 0.6936322912593664, ACC: 0.6364303537355056, RMSE: 0.4798025736138998\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.526375228755087, AUC: 0.6945003143050237, ACC: 0.6386320270071921, RMSE: 0.48607423534092714\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 5: 100%|██████████| 1423/1423 [00:05<00:00, 241.30it/s]\n",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.45689579883219905, AUC: 0.6869292016479911, ACC: 0.6337149567004257, RMSE: 0.5018380826781771\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3992782706736429, AUC: 0.6774931837692044, ACC: 0.628724497284603, RMSE: 0.5134459397578205\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 7: 100%|██████████| 1423/1423 [00:06<00:00, 232.20it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3640380170188441, AUC: 0.670362777315834, ACC: 0.6262292675766916, RMSE: 0.5233112679441873\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 8: 100%|██████████| 1423/1423 [00:06<00:00, 234.27it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3440946058009555, AUC: 0.6659717350790819, ACC: 0.6232203141053868, RMSE: 0.5295535551272162\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1423/1423 [00:06<00:00, 235.35it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3317254370213267, AUC: 0.6622753010153815, ACC: 0.6155878467635403, RMSE: 0.5345842969247592\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 379/379 [00:00<00:00, 846.77it/s]\n",
      "[I 2025-01-28 21:36:16,302] Trial 54 finished with value: 0.6622753010153815 and parameters: {'batch_size': 72, 'learning_rate': 0.0024940110134561715}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001553\n",
      "Using device: cuda\n",
      "Seed: 1738071376\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 60\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1707/1707 [00:06<00:00, 282.88it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 0, Loss: 0.849121428175006, AUC: 0.6519079463058568, ACC: 0.5140907089387935, RMSE: 0.5030325668662358\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1707/1707 [00:06<00:00, 244.74it/s]\n",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.694770381963246, AUC: 0.6668914505476002, ACC: 0.5140907089387935, RMSE: 0.5031721742857989\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1707/1707 [00:06<00:00, 244.79it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6549828141037316, AUC: 0.6730509126704218, ACC: 0.574049611037722, RMSE: 0.4888236319892857\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1707/1707 [00:05<00:00, 294.26it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6166278076018237, AUC: 0.6820295145946738, ACC: 0.6235872596506679, RMSE: 0.4815173246102398\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1707/1707 [00:05<00:00, 295.01it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5923450417733709, AUC: 0.6895977273013936, ACC: 0.631439894319683, RMSE: 0.4792358033134544\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1707/1707 [00:06<00:00, 244.43it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5628861411489376, AUC: 0.6946781599567314, ACC: 0.638925583443417, RMSE: 0.4792935667138295\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1707/1707 [00:06<00:00, 243.91it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5238947344929695, AUC: 0.6960035450545012, ACC: 0.6390723616615295, RMSE: 0.48141690099446305\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1707/1707 [00:06<00:00, 243.95it/s]\n",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.4778225149896037, AUC: 0.6926127700659583, ACC: 0.637237633935124, RMSE: 0.4899727297259354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1707/1707 [00:06<00:00, 243.92it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.4318326622701627, AUC: 0.6859447493704467, ACC: 0.6354029062087186, RMSE: 0.5008568153019048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1707/1707 [00:07<00:00, 241.51it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3957790820289231, AUC: 0.6795464779564427, ACC: 0.6293849992661089, RMSE: 0.5112721070189646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 455/455 [00:00<00:00, 932.55it/s]\n",
      "[I 2025-01-28 21:37:27,145] Trial 55 finished with value: 0.6795464779564427 and parameters: {'batch_size': 60, 'learning_rate': 0.0015526735913559844}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000597\n",
      "Using device: cuda\n",
      "Seed: 1738071447\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 54\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.4072265341273094, AUC: 0.6073209622962259, ACC: 0.5140907089387935, RMSE: 0.5039059575410647\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6955526281672525, AUC: 0.6531950067814638, ACC: 0.5140907089387935, RMSE: 0.5043359231609325\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6955889077895431, AUC: 0.6636385902964212, ACC: 0.5140907089387935, RMSE: 0.5043633036088645\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6944980003925518, AUC: 0.6667048834839853, ACC: 0.5140907089387935, RMSE: 0.5040951792795723\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6916478646385965, AUC: 0.668218776132568, ACC: 0.5140907089387935, RMSE: 0.5030163216654777\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6833471113214005, AUC: 0.6693670917735095, ACC: 0.5140907089387935, RMSE: 0.5008996240177741\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6660154562406183, AUC: 0.6707415604169072, ACC: 0.5328783208571848, RMSE: 0.4952524607260978\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6429105781698202, AUC: 0.6731285321583469, ACC: 0.5650961397328637, RMSE: 0.48920283606878256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1897/1897 [00:06<00:00, 315.75it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6222446948274161, AUC: 0.6763331497416833, ACC: 0.5981212388081608, RMSE: 0.4837175367704007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1897/1897 [00:05<00:00, 374.31it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6070578488539443, AUC: 0.6800558774069183, ACC: 0.6186701893439014, RMSE: 0.48027448263596106\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 506/506 [00:00<00:00, 984.32it/s]\n",
      "[I 2025-01-28 21:38:43,062] Trial 56 finished with value: 0.6800558774069183 and parameters: {'batch_size': 54, 'learning_rate': 0.0005972829776034336}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000784\n",
      "Using device: cuda\n",
      "Seed: 1738071523\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 45\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9350878495873592, AUC: 0.6330107704585834, ACC: 0.5140907089387935, RMSE: 0.5041370503214352\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6972621882920316, AUC: 0.6626172364206593, ACC: 0.5140907089387935, RMSE: 0.5040615436658196\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6950983057986873, AUC: 0.6669134211748766, ACC: 0.5140907089387935, RMSE: 0.5031425892358676\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6851565966051995, AUC: 0.6687781646893641, ACC: 0.5140907089387935, RMSE: 0.4997908834569944\n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6589073329666377, AUC: 0.6713731350974733, ACC: 0.5554087773374431, RMSE: 0.49154962694742516\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 5: 100%|██████████| 2276/2276 [00:09<00:00, 244.16it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6290300481139252, AUC: 0.6756730606797893, ACC: 0.600543079407016, RMSE: 0.4838860051828741\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6095776505152873, AUC: 0.6809108129444726, ACC: 0.6255687655951857, RMSE: 0.479278137022022\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2276/2276 [00:08<00:00, 267.10it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5943899771136433, AUC: 0.6857785035199899, ACC: 0.6317334507559078, RMSE: 0.4778604426578461\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 8: 100%|██████████| 2276/2276 [00:07<00:00, 288.02it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5793901224520798, AUC: 0.6897676492970424, ACC: 0.6365771319536181, RMSE: 0.4759841223412054\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2276/2276 [00:08<00:00, 277.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.561260095406291, AUC: 0.6928772908125154, ACC: 0.6403199765154851, RMSE: 0.47625322101185297\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 607/607 [00:00<00:00, 1060.53it/s]\n",
      "[I 2025-01-28 21:40:04,879] Trial 57 finished with value: 0.6928772908125154 and parameters: {'batch_size': 45, 'learning_rate': 0.0007838243798343074}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001194\n",
      "Using device: cuda\n",
      "Seed: 1738071604\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 81\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1265/1265 [00:04<00:00, 273.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0379598626035005, AUC: 0.6332308971702415, ACC: 0.5140907089387935, RMSE: 0.5070685817268458\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1265/1265 [00:05<00:00, 237.65it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6965768820212292, AUC: 0.6634311953368799, ACC: 0.5140907089387935, RMSE: 0.5068915935592053\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1265/1265 [00:05<00:00, 241.97it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6929790274427814, AUC: 0.6676407373377011, ACC: 0.5140907089387935, RMSE: 0.5053313096391743\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1265/1265 [00:04<00:00, 256.03it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6736429218011412, AUC: 0.6701524392840421, ACC: 0.5308234258036107, RMSE: 0.4979644315975412\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1265/1265 [00:05<00:00, 241.80it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6353948380871723, AUC: 0.6754919916632357, ACC: 0.5908557170115954, RMSE: 0.4861280290419866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1265/1265 [00:05<00:00, 235.98it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6088330663004412, AUC: 0.6821570153840746, ACC: 0.6242477616321738, RMSE: 0.4806899912314691\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1265/1265 [00:05<00:00, 237.42it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5896456854616701, AUC: 0.6880077352131911, ACC: 0.6318802289740203, RMSE: 0.4776310312732913\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1265/1265 [00:05<00:00, 237.55it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5686657460310713, AUC: 0.6924866707395336, ACC: 0.6387788052253046, RMSE: 0.4768447182427587\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1265/1265 [00:05<00:00, 236.01it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5437020320194983, AUC: 0.6952520374846067, ACC: 0.6397328636430354, RMSE: 0.4768689168989469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1265/1265 [00:05<00:00, 241.91it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.515331129005304, AUC: 0.6960390667507113, ACC: 0.639292528988698, RMSE: 0.4797457952735615\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 337/337 [00:00<00:00, 793.10it/s]\n",
      "[I 2025-01-28 21:41:00,750] Trial 58 finished with value: 0.6960390667507113 and parameters: {'batch_size': 81, 'learning_rate': 0.0011937186727305974}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000462\n",
      "Using device: cuda\n",
      "Seed: 1738071660\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 82\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1249/1249 [00:05<00:00, 241.77it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.7869924451944064, AUC: 0.5811167201971621, ACC: 0.5140907089387935, RMSE: 0.5014065878806818\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1249/1249 [00:05<00:00, 240.92it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6937961799798725, AUC: 0.6186365360751125, ACC: 0.5140907089387935, RMSE: 0.5016521252545694\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1249/1249 [00:05<00:00, 240.53it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6937821640598001, AUC: 0.6491256002978001, ACC: 0.5140907089387935, RMSE: 0.501876171629662\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6938779704851376, AUC: 0.6616098001503016, ACC: 0.5140907089387935, RMSE: 0.5019230845422585\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6935707066324446, AUC: 0.6658853618377967, ACC: 0.5140907089387935, RMSE: 0.5017448224052029\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1249/1249 [00:05<00:00, 240.61it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6930118669406045, AUC: 0.6678331258629104, ACC: 0.5140907089387935, RMSE: 0.5017310652460375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1249/1249 [00:05<00:00, 240.47it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6920346135230327, AUC: 0.6689858507219852, ACC: 0.5140907089387935, RMSE: 0.5014150718745688\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1249/1249 [00:05<00:00, 240.39it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.690098341080167, AUC: 0.6697557497983241, ACC: 0.5140907089387935, RMSE: 0.5009015268945808\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1249/1249 [00:05<00:00, 240.77it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6868275684581173, AUC: 0.6704547887504783, ACC: 0.5140907089387935, RMSE: 0.5001216678138827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1249/1249 [00:05<00:00, 240.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6809621636060069, AUC: 0.6710523510026551, ACC: 0.5140907089387935, RMSE: 0.4985760311895686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 333/333 [00:00<00:00, 785.94it/s]\n",
      "[I 2025-01-28 21:41:56,686] Trial 59 finished with value: 0.6710523510026551 and parameters: {'batch_size': 82, 'learning_rate': 0.000462405781150603}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000012\n",
      "Using device: cuda\n",
      "Seed: 1738071716\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 102\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 28.821860820767412, AUC: 0.5, ACC: 0.5140907089387935, RMSE: 0.6970716918021604\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 1, Loss: 6.421770041504229, AUC: 0.49813389814447384, ACC: 0.5140907089387935, RMSE: 0.6970609719206526\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 2, Loss: 4.355430999095459, AUC: 0.48883332627211606, ACC: 0.5140907089387935, RMSE: 0.6967344471338792\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 3.0358688882681952, AUC: 0.489064233468208, ACC: 0.5140907089387935, RMSE: 0.6940240902386899\n"
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    },
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     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1004/1004 [00:04<00:00, 237.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 2.2240871298034115, AUC: 0.4901995047229841, ACC: 0.5140907089387935, RMSE: 0.6853905633138397\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1004/1004 [00:04<00:00, 237.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 1.7175835672009039, AUC: 0.4908843349966543, ACC: 0.5140907089387935, RMSE: 0.6691872592231657\n",
      "[NCDM] Early stopping at epoch 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 268/268 [00:00<00:00, 687.75it/s]\n",
      "[I 2025-01-28 21:42:20,039] Trial 60 finished with value: 0.4908843349966543 and parameters: {'batch_size': 102, 'learning_rate': 1.151399924992406e-05}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000999\n",
      "Using device: cuda\n",
      "Seed: 1738071740\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 73\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1403/1403 [00:03<00:00, 396.10it/s]\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.1189617665852298, AUC: 0.6283221760709683, ACC: 0.5140907089387935, RMSE: 0.5051590953745242\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1403/1403 [00:04<00:00, 301.26it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6967341983989571, AUC: 0.6622914286200947, ACC: 0.5140907089387935, RMSE: 0.5055980255963048\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1403/1403 [00:03<00:00, 424.47it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6947661841756857, AUC: 0.6668224015448003, ACC: 0.5140907089387935, RMSE: 0.505228203851656\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1403/1403 [00:03<00:00, 424.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6848231817005536, AUC: 0.6687999736093742, ACC: 0.5140907089387935, RMSE: 0.5018093136450744\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1403/1403 [00:03<00:00, 425.73it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6571015537167479, AUC: 0.6715928413702383, ACC: 0.5575370615000734, RMSE: 0.491795757671361\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1403/1403 [00:03<00:00, 426.01it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6258869445162506, AUC: 0.6766065643878987, ACC: 0.6041391457507705, RMSE: 0.48286872410584303\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6043132453710797, AUC: 0.6823983365281299, ACC: 0.6257155438132981, RMSE: 0.4791058778183269\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1403/1403 [00:04<00:00, 291.97it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5874226401936388, AUC: 0.6876553642989813, ACC: 0.6337149567004257, RMSE: 0.47709029380336326\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1403/1403 [00:05<00:00, 240.25it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.569668259118509, AUC: 0.6917280157084595, ACC: 0.637237633935124, RMSE: 0.47602446947526045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1403/1403 [00:05<00:00, 237.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5497502602599641, AUC: 0.6946874635147979, ACC: 0.6400264200792602, RMSE: 0.4758241713365393\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 374/374 [00:00<00:00, 840.30it/s]\n",
      "[I 2025-01-28 21:43:05,246] Trial 61 finished with value: 0.6946874635147979 and parameters: {'batch_size': 73, 'learning_rate': 0.0009988596063928155}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002054\n",
      "Using device: cuda\n",
      "Seed: 1738071785\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 62\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1652/1652 [00:06<00:00, 242.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.1873826999713375, AUC: 0.6562661404065386, ACC: 0.5140907089387935, RMSE: 0.49957482453942004\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1652/1652 [00:06<00:00, 241.01it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6793152496363003, AUC: 0.6684206838255327, ACC: 0.552619991193307, RMSE: 0.4959097179853689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1652/1652 [00:06<00:00, 242.12it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6266585055549266, AUC: 0.6805030626817252, ACC: 0.6194774695435198, RMSE: 0.4834925155705591\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.596285195998649, AUC: 0.6901807898020067, ACC: 0.6334214002642008, RMSE: 0.4796759691875159\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5554884178406101, AUC: 0.6954291069414353, ACC: 0.6357698517539997, RMSE: 0.48084740767659084\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5010716297435991, AUC: 0.6931396231207325, ACC: 0.6356964626449435, RMSE: 0.49002168771653987\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1652/1652 [00:05<00:00, 328.25it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.4413465461434377, AUC: 0.6851782461467044, ACC: 0.6307060032291208, RMSE: 0.5024392031002219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "[NCDM] Epoch 7: 100%|██████████| 1652/1652 [00:05<00:00, 317.63it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3933936128207904, AUC: 0.677092085065353, ACC: 0.6272567151034787, RMSE: 0.5144487839494202\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1652/1652 [00:05<00:00, 328.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3619416614466732, AUC: 0.669855749571934, ACC: 0.6241743725231176, RMSE: 0.5239080944558535\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1652/1652 [00:05<00:00, 320.02it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3438200002951101, AUC: 0.6656537323492475, ACC: 0.622926757669162, RMSE: 0.5295363104073001\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 440/440 [00:00<00:00, 916.75it/s]\n",
      "[I 2025-01-28 21:44:07,393] Trial 62 finished with value: 0.6656537323492475 and parameters: {'batch_size': 62, 'learning_rate': 0.002053772631856177}. Best is trial 52 with value: 0.696188915484344.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001217\n",
      "Using device: cuda\n",
      "Seed: 1738071847\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 88\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0263280732320346, AUC: 0.6269338437245884, ACC: 0.5140907089387935, RMSE: 0.5052772087815497\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6964813355113223, AUC: 0.6617444268399135, ACC: 0.5140907089387935, RMSE: 0.5060423328147038\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch: 2, Loss: 0.6933006816303607, AUC: 0.666608301123941, ACC: 0.5140907089387935, RMSE: 0.5049876048915635\n"
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    },
    {
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6761714278934747, AUC: 0.6694402157994253, ACC: 0.5249522970791135, RMSE: 0.4988144851694556\n"
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    },
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6377909034490585, AUC: 0.6745504025918009, ACC: 0.5879201526493468, RMSE: 0.4863731181155399\n"
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6105008730568837, AUC: 0.6813645204123622, ACC: 0.6254953764861294, RMSE: 0.47979522647542433\n"
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    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5901027469546934, AUC: 0.6876452522045821, ACC: 0.6326875091736386, RMSE: 0.4772990222805825\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1164/1164 [00:04<00:00, 239.10it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5452663557175099, AUC: 0.6951465612249045, ACC: 0.6398796418611478, RMSE: 0.4769585957540601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1164/1164 [00:04<00:00, 239.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5179316917645562, AUC: 0.6962533288788371, ACC: 0.6390723616615295, RMSE: 0.4792804449881654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 310/310 [00:00<00:00, 753.79it/s]\n",
      "[I 2025-01-28 21:44:52,893] Trial 63 finished with value: 0.6962533288788371 and parameters: {'batch_size': 88, 'learning_rate': 0.0012165862778976651}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001637\n",
      "Using device: cuda\n",
      "Seed: 1738071892\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 88\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1164/1164 [00:02<00:00, 404.89it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.219730032934356, AUC: 0.6440324251098613, ACC: 0.5140907089387935, RMSE: 0.507976649186765\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1164/1164 [00:02<00:00, 403.89it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6967612351850956, AUC: 0.6652033129291104, ACC: 0.5140907089387935, RMSE: 0.5061675935122093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1164/1164 [00:03<00:00, 321.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6773320948032989, AUC: 0.6691975794362691, ACC: 0.5353001614560399, RMSE: 0.4983819742785581\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1164/1164 [00:03<00:00, 314.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6310862043674049, AUC: 0.6771823500615188, ACC: 0.6052399823866138, RMSE: 0.4843917249059958\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1164/1164 [00:03<00:00, 307.06it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6019092188616798, AUC: 0.6857760132280856, ACC: 0.6296785557023338, RMSE: 0.47900166439859154\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1164/1164 [00:03<00:00, 305.20it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5763864110118335, AUC: 0.6919797184590246, ACC: 0.6386320270071921, RMSE: 0.4776102314455205\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 6: 100%|██████████| 1164/1164 [00:03<00:00, 317.95it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5438464086797229, AUC: 0.695051434230259, ACC: 0.6375311903713489, RMSE: 0.4779727687141047\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1164/1164 [00:03<00:00, 325.53it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5041359573397849, AUC: 0.6945736323796593, ACC: 0.6374578012622927, RMSE: 0.48272906396142545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1164/1164 [00:03<00:00, 320.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.4601119829668212, AUC: 0.6906886584236926, ACC: 0.6322471745193013, RMSE: 0.4915566483881901\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1164/1164 [00:03<00:00, 309.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.42058879385391873, AUC: 0.68513700001326, ACC: 0.6304858359019522, RMSE: 0.5018640374376748\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 310/310 [00:00<00:00, 753.11it/s]\n",
      "[I 2025-01-28 21:45:31,934] Trial 64 finished with value: 0.68513700001326 and parameters: {'batch_size': 88, 'learning_rate': 0.0016365619120360542}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.003906\n",
      "Using device: cuda\n",
      "Seed: 1738071931\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 81\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1265/1265 [00:03<00:00, 373.54it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8656664525567307, AUC: 0.663193981557394, ACC: 0.5264200792602378, RMSE: 0.49807033671796597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1265/1265 [00:03<00:00, 352.44it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6382426362263827, AUC: 0.6819626863716675, ACC: 0.6216791428152062, RMSE: 0.48388867854486906\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1265/1265 [00:03<00:00, 357.43it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.5882425655489383, AUC: 0.6933485165676102, ACC: 0.6364303537355056, RMSE: 0.4816330590247315\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5051561436045311, AUC: 0.6875891936855253, ACC: 0.6315866725377954, RMSE: 0.4959686428979773\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1265/1265 [00:03<00:00, 416.85it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.4171364145081034, AUC: 0.6748513182538073, ACC: 0.6271833259944224, RMSE: 0.5161438771448379\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1265/1265 [00:03<00:00, 416.98it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3692817971345935, AUC: 0.6671216031097816, ACC: 0.6188903566710701, RMSE: 0.5271490037897708\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1265/1265 [00:03<00:00, 415.15it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3500059279528531, AUC: 0.6631178389958367, ACC: 0.6048730368413328, RMSE: 0.5362263736490168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1265/1265 [00:03<00:00, 413.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3449483567900337, AUC: 0.6595394943586264, ACC: 0.6138265081461911, RMSE: 0.5327224064832229\n",
      "[NCDM] Early stopping at epoch 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 337/337 [00:00<00:00, 794.46it/s]\n",
      "[I 2025-01-28 21:46:01,285] Trial 65 finished with value: 0.6595394943586264 and parameters: {'batch_size': 81, 'learning_rate': 0.003905645981163997}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000616\n",
      "Using device: cuda\n",
      "Seed: 1738071961\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 75\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.2962023386319943, AUC: 0.590595418013823, ACC: 0.5140907089387935, RMSE: 0.502209081871171\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6944644538846582, AUC: 0.6360491529719478, ACC: 0.5140907089387935, RMSE: 0.5034377196131561\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6948813592270562, AUC: 0.6586534679039645, ACC: 0.5140907089387935, RMSE: 0.5034286335646608\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6944739335539749, AUC: 0.6641458444304945, ACC: 0.5140907089387935, RMSE: 0.5032887396607555\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.693078724042912, AUC: 0.6666564036454854, ACC: 0.5140907089387935, RMSE: 0.5031073238232586\n"
     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6897167329994163, AUC: 0.668211520866544, ACC: 0.5140907089387935, RMSE: 0.5021421186895397\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6817664460416769, AUC: 0.6693216412511357, ACC: 0.5140907089387935, RMSE: 0.4993585881402436\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6667237996095096, AUC: 0.6707134772549566, ACC: 0.5328049317481286, RMSE: 0.49500822746175127\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6455278054076092, AUC: 0.672734731842457, ACC: 0.5625275209158961, RMSE: 0.4898088463856071\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6253122931038129, AUC: 0.6753982984730199, ACC: 0.5874064288859533, RMSE: 0.4847261377107332\n"
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     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 364/364 [00:00<00:00, 827.26it/s]\n",
      "[I 2025-01-28 21:46:51,570] Trial 66 finished with value: 0.6753982984730199 and parameters: {'batch_size': 75, 'learning_rate': 0.0006163759762867578}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000022\n",
      "Using device: cuda\n",
      "Seed: 1738072011\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 91\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1126/1126 [00:03<00:00, 323.71it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 8.568421773016983, AUC: 0.4943181133376046, ACC: 0.5140907089387935, RMSE: 0.6970223547420861\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1126/1126 [00:04<00:00, 268.72it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 3.028970841007377, AUC: 0.5014227975551155, ACC: 0.5140907089387935, RMSE: 0.6881225961910475\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1126/1126 [00:04<00:00, 263.01it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 1.6102312593911088, AUC: 0.5030005322325164, ACC: 0.5140907089387935, RMSE: 0.6480733509410721\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1126/1126 [00:04<00:00, 281.01it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 1.0827686846203322, AUC: 0.5039800147067368, ACC: 0.5140907089387935, RMSE: 0.5907383991794523\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1126/1126 [00:04<00:00, 237.72it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.8484024212267217, AUC: 0.5049090660747749, ACC: 0.5140907089387935, RMSE: 0.5432320528482065\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1126/1126 [00:04<00:00, 250.60it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.7508575875530447, AUC: 0.5054113935274618, ACC: 0.5140907089387935, RMSE: 0.5193765482529892\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1126/1126 [00:04<00:00, 241.28it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.7151785564888434, AUC: 0.5054686486802908, ACC: 0.5140907089387935, RMSE: 0.5083692489694014\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1126/1126 [00:04<00:00, 241.34it/s]\n",
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     ]
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.7009528690608314, AUC: 0.505739971912526, ACC: 0.5140907089387935, RMSE: 0.5038659652125697\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1126/1126 [00:04<00:00, 241.69it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6959771018468677, AUC: 0.5078439235961195, ACC: 0.5140907089387935, RMSE: 0.5018920145728785\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1126/1126 [00:04<00:00, 279.01it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6936558680360736, AUC: 0.508908410190102, ACC: 0.5140907089387935, RMSE: 0.501024568403394\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 300/300 [00:00<00:00, 739.84it/s]\n",
      "[I 2025-01-28 21:47:38,590] Trial 67 finished with value: 0.508908410190102 and parameters: {'batch_size': 91, 'learning_rate': 2.1711918815171543e-05}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002442\n",
      "Using device: cuda\n",
      "Seed: 1738072058\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 85\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1205/1205 [00:03<00:00, 338.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8686471764477457, AUC: 0.6563497215023554, ACC: 0.5140907089387935, RMSE: 0.5066015841431255\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1205/1205 [00:03<00:00, 377.09it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6809834652174558, AUC: 0.6685924492840196, ACC: 0.5520328783208572, RMSE: 0.4964659146062338\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1205/1205 [00:03<00:00, 365.32it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6246335837108961, AUC: 0.6818615977691296, ACC: 0.6213855863789813, RMSE: 0.4827266130228581\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1205/1205 [00:04<00:00, 274.92it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5906861383143303, AUC: 0.6919429354461358, ACC: 0.635549684426831, RMSE: 0.47962461079378477\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5432725265807631, AUC: 0.6954722504401402, ACC: 0.6374578012622927, RMSE: 0.48204201162902177\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1205/1205 [00:04<00:00, 271.74it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.4803445788339955, AUC: 0.6907427656750669, ACC: 0.6370174666079553, RMSE: 0.4919472576237472\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1205/1205 [00:04<00:00, 272.90it/s]\n",
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    {
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.41974964450998425, AUC: 0.682285518758528, ACC: 0.63246734184647, RMSE: 0.5068242978471353\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1205/1205 [00:04<00:00, 264.25it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3770965160546956, AUC: 0.6740061929570879, ACC: 0.6248348745046235, RMSE: 0.5180715688602187\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1205/1205 [00:04<00:00, 267.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3539115822166823, AUC: 0.6687330914839456, ACC: 0.6209452517246441, RMSE: 0.5262972301504956\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1205/1205 [00:04<00:00, 268.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3404212619382811, AUC: 0.6653337999126996, ACC: 0.6200645824159695, RMSE: 0.5307126109142373\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 321/321 [00:00<00:00, 765.91it/s]\n",
      "[I 2025-01-28 21:48:23,670] Trial 68 finished with value: 0.6653337999126996 and parameters: {'batch_size': 85, 'learning_rate': 0.002442078574812122}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000218\n",
      "Using device: cuda\n",
      "Seed: 1738072103\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 106\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.557070628656103, AUC: 0.5185668402432466, ACC: 0.5140907089387935, RMSE: 0.515297035111815\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6985308202494017, AUC: 0.527608626586766, ACC: 0.5140907089387935, RMSE: 0.5007740170762697\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6925984436748193, AUC: 0.5448330701278059, ACC: 0.5140907089387935, RMSE: 0.5005114346407524\n"
     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6923468975271286, AUC: 0.5724876431392296, ACC: 0.5140907089387935, RMSE: 0.5006768000448949\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6925269600770589, AUC: 0.6073126721036963, ACC: 0.5140907089387935, RMSE: 0.5008460390523453\n"
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6925807709284627, AUC: 0.6347192831926535, ACC: 0.5140907089387935, RMSE: 0.5008601669985088\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6925858846618076, AUC: 0.6505090275237626, ACC: 0.5140907089387935, RMSE: 0.5008823393141416\n"
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    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch: 8, Loss: 0.6925016804343049, AUC: 0.6629366729549233, ACC: 0.5140907089387935, RMSE: 0.5009480946489858\n"
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 258/258 [00:00<00:00, 678.30it/s]\n",
      "[I 2025-01-28 21:49:01,820] Trial 69 finished with value: 0.6655694828633958 and parameters: {'batch_size': 106, 'learning_rate': 0.0002177295444401951}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.009426\n",
      "Using device: cuda\n",
      "Seed: 1738072141\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 113\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 907/907 [00:03<00:00, 251.40it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 642.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.7281120770427069, AUC: 0.6761903191034173, ACC: 0.6104506091296051, RMSE: 0.48581817899086904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 907/907 [00:03<00:00, 256.08it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 644.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6049744267645061, AUC: 0.6922941097265736, ACC: 0.6391457507705857, RMSE: 0.4833405362104714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 907/907 [00:03<00:00, 237.07it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 630.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.47382219472557074, AUC: 0.6716946630457177, ACC: 0.6157346249816528, RMSE: 0.5153629766416885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 907/907 [00:03<00:00, 236.59it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.39755638006175104, AUC: 0.666443963419229, ACC: 0.6130192279465727, RMSE: 0.5241714355424245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 907/907 [00:03<00:00, 240.74it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 632.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.36892725230741924, AUC: 0.66250855835708, ACC: 0.6194040804344635, RMSE: 0.5327935176024341\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 907/907 [00:03<00:00, 240.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 645.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.35148922997180787, AUC: 0.6572928306220954, ACC: 0.6033318655511523, RMSE: 0.5405491251441966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 907/907 [00:03<00:00, 253.41it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 121/121 [00:00<00:00, 644.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.34544446418737496, AUC: 0.654887381130336, ACC: 0.5866725377953912, RMSE: 0.5544376206900163\n",
      "[NCDM] Early stopping at epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 242/242 [00:00<00:00, 641.46it/s]\n",
      "[I 2025-01-28 21:49:30,764] Trial 70 finished with value: 0.654887381130336 and parameters: {'batch_size': 113, 'learning_rate': 0.009425741203178689}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001255\n",
      "Using device: cuda\n",
      "Seed: 1738072170\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 66\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1552/1552 [00:04<00:00, 329.37it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0577439529072379, AUC: 0.6434810356725152, ACC: 0.5140907089387935, RMSE: 0.5069907941281456\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1552/1552 [00:05<00:00, 289.91it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6973845101508898, AUC: 0.6640530460204866, ACC: 0.5140907089387935, RMSE: 0.5060064395251038\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1552/1552 [00:05<00:00, 290.09it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6862582388458792, AUC: 0.6677061856587001, ACC: 0.5142374871569059, RMSE: 0.5019742189083847\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1552/1552 [00:05<00:00, 287.04it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6487951067458723, AUC: 0.6726218278289797, ACC: 0.5730955526199912, RMSE: 0.4888291512769157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1552/1552 [00:05<00:00, 291.32it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6157326579823629, AUC: 0.6801193205578124, ACC: 0.6203581388521944, RMSE: 0.4819727940975957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1552/1552 [00:05<00:00, 282.13it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5948645491537052, AUC: 0.6869917607991616, ACC: 0.6312931161015706, RMSE: 0.47861903654812366\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1552/1552 [00:06<00:00, 256.81it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.572070186304831, AUC: 0.6919254602808683, ACC: 0.637604579480405, RMSE: 0.4774227607469855\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1552/1552 [00:06<00:00, 254.52it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5457520792356778, AUC: 0.6947601131993988, ACC: 0.638925583443417, RMSE: 0.4782911112837152\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1552/1552 [00:06<00:00, 250.11it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 870.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5124581541443608, AUC: 0.6950694484197482, ACC: 0.6394393072068105, RMSE: 0.48143073755881816\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1552/1552 [00:06<00:00, 235.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 855.14it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.47678212554568483, AUC: 0.6927854949875598, ACC: 0.6371642448260678, RMSE: 0.48811039781079424\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 414/414 [00:00<00:00, 887.23it/s]\n",
      "[I 2025-01-28 21:50:31,433] Trial 71 finished with value: 0.6927854949875598 and parameters: {'batch_size': 66, 'learning_rate': 0.0012552329026148056}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000815\n",
      "Using device: cuda\n",
      "Seed: 1738072231\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 78\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1313/1313 [00:05<00:00, 238.09it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 175/175 [00:00<00:00, 784.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.221194122233467, AUC: 0.6114211686239176, ACC: 0.5140907089387935, RMSE: 0.5031056187476259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1313/1313 [00:04<00:00, 262.60it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.695465897751145, AUC: 0.6538008161042326, ACC: 0.5140907089387935, RMSE: 0.5040784358948978\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1313/1313 [00:05<00:00, 259.51it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.695333606218075, AUC: 0.664015971934518, ACC: 0.5140907089387935, RMSE: 0.5042766377324249\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1313/1313 [00:05<00:00, 259.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.693299282577686, AUC: 0.6673497828433118, ACC: 0.5140907089387935, RMSE: 0.5036234309021957\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1313/1313 [00:04<00:00, 267.88it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.686547491492339, AUC: 0.6689234532780812, ACC: 0.5140907089387935, RMSE: 0.5015330092305266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1313/1313 [00:04<00:00, 273.81it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6685726604930154, AUC: 0.670770419773737, ACC: 0.5317774842213415, RMSE: 0.49567242187817206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1313/1313 [00:04<00:00, 283.36it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6407354981365407, AUC: 0.6739222992272225, ACC: 0.5678849258769999, RMSE: 0.4883773462871347\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1313/1313 [00:04<00:00, 280.55it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6175228277872468, AUC: 0.6780557633493931, ACC: 0.6083957140760311, RMSE: 0.4821001900499449\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1313/1313 [00:04<00:00, 277.01it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 175/175 [00:00<00:00, 805.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6003647882885725, AUC: 0.6826106042666353, ACC: 0.6268163804491413, RMSE: 0.4788315620021838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1313/1313 [00:04<00:00, 277.19it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 175/175 [00:00<00:00, 802.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5870414253463295, AUC: 0.6868466878201333, ACC: 0.6323939527374137, RMSE: 0.4770440332386158\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 350/350 [00:00<00:00, 804.28it/s]\n",
      "[I 2025-01-28 21:51:24,537] Trial 72 finished with value: 0.6868466878201333 and parameters: {'batch_size': 78, 'learning_rate': 0.0008145970258926841}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001312\n",
      "Using device: cuda\n",
      "Seed: 1738072284\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 68\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1506/1506 [00:05<00:00, 269.85it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9562913452090812, AUC: 0.6420477487060454, ACC: 0.5140907089387935, RMSE: 0.5072506045366839\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
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      "[NCDM] Epoch: 1, Loss: 0.6972657977584824, AUC: 0.664397827473655, ACC: 0.5140907089387935, RMSE: 0.5060017611679778\n"
     ]
    },
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      "[NCDM] Epoch: 2, Loss: 0.6837001991659838, AUC: 0.6683913070054499, ACC: 0.5181271099368854, RMSE: 0.4998307608421403\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6432530803786489, AUC: 0.6744393420411617, ACC: 0.5858652575957728, RMSE: 0.4863714666384967\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6123548211012861, AUC: 0.6821298054413634, ACC: 0.6257155438132981, RMSE: 0.48044276122237195\n"
     ]
    },
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     "output_type": "stream",
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      "[NCDM] Epoch 5: 100%|██████████| 1506/1506 [00:05<00:00, 255.74it/s]\n",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5904779973455951, AUC: 0.6891650633391191, ACC: 0.6335681784823132, RMSE: 0.4776473670926628\n"
     ]
    },
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     "output_type": "stream",
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      "[NCDM] Epoch 6: 100%|██████████| 1506/1506 [00:06<00:00, 235.22it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5667649422825412, AUC: 0.6940143300667387, ACC: 0.6399530309702041, RMSE: 0.4767284242793663\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 7: 100%|██████████| 1506/1506 [00:06<00:00, 249.40it/s]\n",
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5369370822531293, AUC: 0.6963372010477338, ACC: 0.6406869220607662, RMSE: 0.478641523866566\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 8: 100%|██████████| 1506/1506 [00:06<00:00, 235.02it/s]\n",
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     "text": [
      "[NCDM] Epoch: 8, Loss: 0.502062221051529, AUC: 0.6958337093027279, ACC: 0.6378247468075737, RMSE: 0.48257895990059846\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1506/1506 [00:05<00:00, 268.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.4643215351251967, AUC: 0.6924154440788782, ACC: 0.6357698517539997, RMSE: 0.48994498848535795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 402/402 [00:00<00:00, 872.14it/s]\n",
      "[I 2025-01-28 21:52:28,039] Trial 73 finished with value: 0.6924154440788782 and parameters: {'batch_size': 68, 'learning_rate': 0.0013122017052656604}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000839\n",
      "Using device: cuda\n",
      "Seed: 1738072348\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 54\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9801834475761598, AUC: 0.6299898631104868, ACC: 0.5140907089387935, RMSE: 0.5052551768183701\n"
     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6970886895074678, AUC: 0.6624229181887364, ACC: 0.5140907089387935, RMSE: 0.5055073781937608\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1897/1897 [00:07<00:00, 247.90it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6951338673932463, AUC: 0.6671380002266057, ACC: 0.5140907089387935, RMSE: 0.5043763058035688\n"
     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6860918637696981, AUC: 0.6688923084585514, ACC: 0.5140907089387935, RMSE: 0.5012504623257124\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6619483848025313, AUC: 0.6712995151692736, ACC: 0.5453544693967415, RMSE: 0.49399894014601814\n"
     ]
    },
    {
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     "text": [
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6310848075588188, AUC: 0.6756306610345104, ACC: 0.585938646704829, RMSE: 0.4862320732272711\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6099727074755577, AUC: 0.680840847600496, ACC: 0.6203581388521944, RMSE: 0.48105269890165575\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5947908646788921, AUC: 0.6858881410466837, ACC: 0.6304858359019522, RMSE: 0.4783400246993634\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 8: 100%|██████████| 1897/1897 [00:06<00:00, 300.04it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5790264655648877, AUC: 0.6901428640577678, ACC: 0.6339351240275943, RMSE: 0.4767904999980743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1897/1897 [00:06<00:00, 292.31it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5618644449889314, AUC: 0.693493751253905, ACC: 0.639292528988698, RMSE: 0.47665134845396673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 506/506 [00:00<00:00, 979.12it/s]\n",
      "[I 2025-01-28 21:53:42,059] Trial 74 finished with value: 0.693493751253905 and parameters: {'batch_size': 54, 'learning_rate': 0.00083921915082187}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001775\n",
      "Using device: cuda\n",
      "Seed: 1738072422\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 37\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2768/2768 [00:09<00:00, 295.31it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8531936775634571, AUC: 0.6636841809650926, ACC: 0.4859092910612065, RMSE: 0.5044340399044742\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6667218073190464, AUC: 0.6721564774379014, ACC: 0.6177895200352268, RMSE: 0.48132312968984525\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2768/2768 [00:08<00:00, 312.76it/s]\n",
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    {
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6206492540464236, AUC: 0.6827313780337496, ACC: 0.629091442829884, RMSE: 0.4762977326980506\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2768/2768 [00:09<00:00, 307.33it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.593382949526341, AUC: 0.6909772196505376, ACC: 0.6384852487890798, RMSE: 0.4759915977841041\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.551801953545497, AUC: 0.6952915695210262, ACC: 0.6394393072068105, RMSE: 0.4801171220705772\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.4951888047094118, AUC: 0.6919481639810863, ACC: 0.63789813591663, RMSE: 0.490672256621332\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.43600711536276116, AUC: 0.6827731955328691, ACC: 0.630779392338177, RMSE: 0.5052453070443939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.3897508612317114, AUC: 0.6740908305403793, ACC: 0.6260091002495229, RMSE: 0.5170992479007722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2768/2768 [00:10<00:00, 255.41it/s]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.35998800307420764, AUC: 0.6677758060271748, ACC: 0.6237340378687802, RMSE: 0.5256171452199115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2768/2768 [00:11<00:00, 245.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.34165859532224907, AUC: 0.6634204471938993, ACC: 0.6221928665785997, RMSE: 0.5316287156918905\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 738/738 [00:00<00:00, 1154.83it/s]\n",
      "[I 2025-01-28 21:55:23,776] Trial 75 finished with value: 0.6634204471938993 and parameters: {'batch_size': 37, 'learning_rate': 0.0017753446432640003}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000476\n",
      "Using device: cuda\n",
      "Seed: 1738072523\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 74\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.2888361848029442, AUC: 0.579793189342715, ACC: 0.5140907089387935, RMSE: 0.5016182103024778\n"
     ]
    },
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6935237668067045, AUC: 0.6153760324604698, ACC: 0.5140907089387935, RMSE: 0.5018388903529192\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6938363856318369, AUC: 0.648160455867877, ACC: 0.5140907089387935, RMSE: 0.5020409195158724\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6939109485015015, AUC: 0.660042781274428, ACC: 0.5140907089387935, RMSE: 0.5020670658084162\n"
     ]
    },
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    },
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6937230473997965, AUC: 0.6640676212354413, ACC: 0.5140907089387935, RMSE: 0.5022098741518585\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1384/1384 [00:05<00:00, 239.06it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6931284140644735, AUC: 0.6661753245276181, ACC: 0.5140907089387935, RMSE: 0.5020607648755852\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1384/1384 [00:05<00:00, 239.41it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6918553962786763, AUC: 0.6675844416479868, ACC: 0.5140907089387935, RMSE: 0.5016487985812298\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1384/1384 [00:05<00:00, 240.10it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6892882663476674, AUC: 0.6686176217151729, ACC: 0.5140907089387935, RMSE: 0.5011002820903179\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1384/1384 [00:05<00:00, 239.94it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6842448853516165, AUC: 0.669407766541279, ACC: 0.5140907089387935, RMSE: 0.4997270586840744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1384/1384 [00:05<00:00, 239.92it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6753657984768036, AUC: 0.6702339505268478, ACC: 0.5149713782474681, RMSE: 0.49742410880928006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 369/369 [00:00<00:00, 831.73it/s]\n",
      "[I 2025-01-28 21:56:24,816] Trial 76 finished with value: 0.6702339505268478 and parameters: {'batch_size': 74, 'learning_rate': 0.00047608950136364224}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001127\n",
      "Using device: cuda\n",
      "Seed: 1738072584\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 61\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1679/1679 [00:06<00:00, 270.30it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9104215818334719, AUC: 0.6471860402213405, ACC: 0.5140907089387935, RMSE: 0.5062706330763218\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1679/1679 [00:06<00:00, 244.05it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6973908123975712, AUC: 0.6645938813635718, ACC: 0.5140907089387935, RMSE: 0.5056721310770639\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1679/1679 [00:06<00:00, 241.33it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6895322517989433, AUC: 0.6679847641569592, ACC: 0.5140907089387935, RMSE: 0.5021880401637513\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1679/1679 [00:06<00:00, 244.08it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6587055877179457, AUC: 0.6717682614129484, ACC: 0.560105680317041, RMSE: 0.4918167324661554\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1679/1679 [00:06<00:00, 244.33it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6232659258858089, AUC: 0.67807277495383, ACC: 0.6067077645677381, RMSE: 0.48354511846291715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1679/1679 [00:06<00:00, 244.78it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6016927849792029, AUC: 0.684735890528924, ACC: 0.6285777190664905, RMSE: 0.47961366723496135\n"
     ]
    },
    {
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     "output_type": "stream",
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      "[NCDM] Epoch 6: 100%|██████████| 1679/1679 [00:06<00:00, 243.60it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5831897578614322, AUC: 0.690000162785315, ACC: 0.6359166299721122, RMSE: 0.47720016142918775\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1679/1679 [00:06<00:00, 242.81it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5607863312111219, AUC: 0.6940269647944954, ACC: 0.6411272567151035, RMSE: 0.47648376938618037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1679/1679 [00:06<00:00, 242.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5327641629370428, AUC: 0.6959034698175004, ACC: 0.6404667547335975, RMSE: 0.47833204584371186\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1679/1679 [00:06<00:00, 242.58it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5018269377957504, AUC: 0.6953854675404465, ACC: 0.6387054161162483, RMSE: 0.48225988722677593\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 448/448 [00:00<00:00, 920.86it/s]\n",
      "[I 2025-01-28 21:57:37,419] Trial 77 finished with value: 0.6953854675404465 and parameters: {'batch_size': 61, 'learning_rate': 0.0011274575801729699}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000669\n",
      "Using device: cuda\n",
      "Seed: 1738072657\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 70\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1463/1463 [00:05<00:00, 264.40it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0669189885676273, AUC: 0.6042091215619283, ACC: 0.5140907089387935, RMSE: 0.5028961227814653\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6949374056074282, AUC: 0.647382331281915, ACC: 0.5140907089387935, RMSE: 0.5035334536083881\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1463/1463 [00:06<00:00, 243.55it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6952007009163346, AUC: 0.6615483406085433, ACC: 0.5140907089387935, RMSE: 0.5034299925473796\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1463/1463 [00:06<00:00, 241.15it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6943878218969578, AUC: 0.6654034418421433, ACC: 0.5140907089387935, RMSE: 0.5031158576637045\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6914936667098138, AUC: 0.6675007204058723, ACC: 0.5140907089387935, RMSE: 0.5027225007781979\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6842756784703743, AUC: 0.6688464051558314, ACC: 0.5140907089387935, RMSE: 0.500969212858141\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1463/1463 [00:06<00:00, 237.39it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6680269706192199, AUC: 0.670350131807593, ACC: 0.5295024218405988, RMSE: 0.49565660611835993\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1463/1463 [00:06<00:00, 243.35it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6449633393276251, AUC: 0.6726994257559357, ACC: 0.5646558050785263, RMSE: 0.4889767072488068\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1463/1463 [00:06<00:00, 240.30it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6233215764908455, AUC: 0.6760335277378092, ACC: 0.6000293556436225, RMSE: 0.48334183970599626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1463/1463 [00:05<00:00, 251.45it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.606769244514421, AUC: 0.679919374913015, ACC: 0.6220460883604872, RMSE: 0.47994288052186473\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 390/390 [00:00<00:00, 853.63it/s]\n",
      "[I 2025-01-28 21:58:41,513] Trial 78 finished with value: 0.679919374913015 and parameters: {'batch_size': 70, 'learning_rate': 0.000668912855944927}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.003004\n",
      "Using device: cuda\n",
      "Seed: 1738072721\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 94\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1090/1090 [00:03<00:00, 275.45it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8535394271305942, AUC: 0.6589289631836754, ACC: 0.5140907089387935, RMSE: 0.5045476819594814\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1090/1090 [00:03<00:00, 283.53it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6663565985653379, AUC: 0.6724881713829669, ACC: 0.580507852634669, RMSE: 0.49045328166680385\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1090/1090 [00:03<00:00, 302.66it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6130967389006133, AUC: 0.6866778654338968, ACC: 0.6284309408483781, RMSE: 0.48218269333290126\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1090/1090 [00:04<00:00, 238.03it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5700008301286522, AUC: 0.6946015538343434, ACC: 0.6363569646264494, RMSE: 0.4818559269576229\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1090/1090 [00:04<00:00, 272.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5007217793289674, AUC: 0.6916913944028371, ACC: 0.6359900190811684, RMSE: 0.4916871206119181\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1090/1090 [00:03<00:00, 299.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.42756669308067463, AUC: 0.6811699757902661, ACC: 0.6287978863936592, RMSE: 0.5060949391629658\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1090/1090 [00:03<00:00, 292.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.37971327630204893, AUC: 0.6734675137108896, ACC: 0.62160575370615, RMSE: 0.5191839598722551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1090/1090 [00:03<00:00, 281.39it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 145/145 [00:00<00:00, 578.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.35590156944246465, AUC: 0.6690579074799421, ACC: 0.6177161309261705, RMSE: 0.5270064423820171\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1090/1090 [00:04<00:00, 263.56it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 145/145 [00:00<00:00, 705.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3451598932300139, AUC: 0.6648221581214618, ACC: 0.6211654190518127, RMSE: 0.5359524023000115\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 291/291 [00:00<00:00, 726.48it/s]\n",
      "[I 2025-01-28 21:59:20,636] Trial 79 finished with value: 0.6648221581214618 and parameters: {'batch_size': 94, 'learning_rate': 0.0030042386297146955}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.005555\n",
      "Using device: cuda\n",
      "Seed: 1738072760\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 80\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1280/1280 [00:04<00:00, 268.88it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 171/171 [00:00<00:00, 778.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.776639725244604, AUC: 0.6712790645903023, ACC: 0.5671510347864377, RMSE: 0.4935600798531714\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1280/1280 [00:05<00:00, 247.44it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 171/171 [00:00<00:00, 777.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6211910361424089, AUC: 0.68946141885621, ACC: 0.6356230735358873, RMSE: 0.4788257239124725\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1280/1280 [00:05<00:00, 240.18it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 171/171 [00:00<00:00, 780.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.539085517602507, AUC: 0.6877264508133389, ACC: 0.6315132834287391, RMSE: 0.4942776937290398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1280/1280 [00:05<00:00, 240.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.4278404040960595, AUC: 0.6726107131495282, ACC: 0.625348598268017, RMSE: 0.519472305690889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1280/1280 [00:05<00:00, 244.65it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.3734993677062448, AUC: 0.6654602010926883, ACC: 0.6189637457801263, RMSE: 0.5292254897632903\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1280/1280 [00:05<00:00, 246.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.3534304113127291, AUC: 0.6621641757818358, ACC: 0.6143402319095846, RMSE: 0.5346671933949164\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1280/1280 [00:05<00:00, 244.72it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 171/171 [00:00<00:00, 776.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.3480454255070072, AUC: 0.6594781965241345, ACC: 0.6177895200352268, RMSE: 0.5343380500258396\n",
      "[NCDM] Early stopping at epoch 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 341/341 [00:00<00:00, 789.37it/s]\n",
      "[I 2025-01-28 22:00:00,052] Trial 80 finished with value: 0.6594781965241345 and parameters: {'batch_size': 80, 'learning_rate': 0.00555523251714147}. Best is trial 63 with value: 0.6962533288788371.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001007\n",
      "Using device: cuda\n",
      "Seed: 1738072800\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 61\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1679/1679 [00:05<00:00, 290.76it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0344736090084141, AUC: 0.6428909550765356, ACC: 0.5140907089387935, RMSE: 0.506788272864485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1679/1679 [00:06<00:00, 262.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6975850268373041, AUC: 0.6656873890216506, ACC: 0.5140907089387935, RMSE: 0.5057836109850677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1679/1679 [00:04<00:00, 389.44it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6935685756413833, AUC: 0.6689325304459747, ACC: 0.5140907089387935, RMSE: 0.503867134088167\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1679/1679 [00:04<00:00, 398.03it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6755970192672668, AUC: 0.6708437270678883, ACC: 0.5258329663877881, RMSE: 0.4977796571416265\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1679/1679 [00:04<00:00, 353.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6404973531209549, AUC: 0.6750211216641273, ACC: 0.5775722882724204, RMSE: 0.4870929357171394\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1679/1679 [00:04<00:00, 352.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6135087814659076, AUC: 0.6806402982485701, ACC: 0.6196976368706884, RMSE: 0.4814186923675869\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1679/1679 [00:04<00:00, 360.13it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5954552788582496, AUC: 0.6862364692792309, ACC: 0.6293849992661089, RMSE: 0.47859533818547007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1679/1679 [00:06<00:00, 250.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5778794012267082, AUC: 0.6910261091474458, ACC: 0.6333480111551446, RMSE: 0.4775935470999268\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1679/1679 [00:05<00:00, 284.01it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 224/224 [00:00<00:00, 931.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5573219476958837, AUC: 0.694393759996878, ACC: 0.6379715250256862, RMSE: 0.47653035806045974\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1679/1679 [00:03<00:00, 451.05it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 224/224 [00:00<00:00, 929.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5332038459535011, AUC: 0.696368680062281, ACC: 0.6363569646264494, RMSE: 0.47876641553427507\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 448/448 [00:00<00:00, 931.01it/s]\n",
      "[I 2025-01-28 22:00:55,797] Trial 81 finished with value: 0.696368680062281 and parameters: {'batch_size': 61, 'learning_rate': 0.0010067931759100532}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000997\n",
      "Using device: cuda\n",
      "Seed: 1738072855\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 65\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1576/1576 [00:03<00:00, 410.89it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9478671089344218, AUC: 0.6360017943038292, ACC: 0.5140907089387935, RMSE: 0.5061315672409646\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1576/1576 [00:05<00:00, 290.59it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.697015513547786, AUC: 0.6632796217257377, ACC: 0.5140907089387935, RMSE: 0.5053197462083275\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1576/1576 [00:04<00:00, 351.29it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6941193280561926, AUC: 0.6669672481336555, ACC: 0.5140907089387935, RMSE: 0.5046079999631523\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1576/1576 [00:05<00:00, 288.88it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6799445194263144, AUC: 0.6690989595646668, ACC: 0.5163657713195362, RMSE: 0.49951080779351326\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1576/1576 [00:05<00:00, 303.24it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6474397032129281, AUC: 0.6731116067977854, ACC: 0.5664905328049318, RMSE: 0.49000722138495867\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1576/1576 [00:04<00:00, 330.67it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6186279653746465, AUC: 0.6786990348555701, ACC: 0.6133861734918538, RMSE: 0.4823973424945319\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1576/1576 [00:04<00:00, 361.40it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5989623464606135, AUC: 0.6847627123741958, ACC: 0.629091442829884, RMSE: 0.47933188717092046\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1576/1576 [00:03<00:00, 442.97it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5812410857471718, AUC: 0.6897985569459147, ACC: 0.6344488477909879, RMSE: 0.47664076493512053\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1576/1576 [00:03<00:00, 413.82it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5624439605904109, AUC: 0.6935070220302433, ACC: 0.6401731982973726, RMSE: 0.4760020206561768\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1576/1576 [00:05<00:00, 297.85it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5401421058639354, AUC: 0.6957272627994266, ACC: 0.6391457507705857, RMSE: 0.4769289546494727\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 420/420 [00:00<00:00, 795.59it/s]\n",
      "[I 2025-01-28 22:01:46,376] Trial 82 finished with value: 0.6957272627994266 and parameters: {'batch_size': 65, 'learning_rate': 0.0009969190286360393}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000916\n",
      "Using device: cuda\n",
      "Seed: 1738072906\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 52\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1970/1970 [00:06<00:00, 291.03it/s]\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0617637102373965, AUC: 0.6340403175025153, ACC: 0.5140907089387935, RMSE: 0.50496668028583\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1970/1970 [00:08<00:00, 240.78it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6977916997247541, AUC: 0.6637668025977949, ACC: 0.5140907089387935, RMSE: 0.5046340537068371\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1970/1970 [00:08<00:00, 239.29it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6936155682439127, AUC: 0.6673187134871731, ACC: 0.5140907089387935, RMSE: 0.5040451445585594\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1970/1970 [00:06<00:00, 294.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6756835996953364, AUC: 0.6695496592773992, ACC: 0.5258329663877881, RMSE: 0.49717674538984463\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1970/1970 [00:08<00:00, 244.13it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6418915532869736, AUC: 0.6736216099553892, ACC: 0.5722148833113166, RMSE: 0.48799038195798156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1970/1970 [00:08<00:00, 242.24it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6164336010586792, AUC: 0.679229779665225, ACC: 0.6190371348891824, RMSE: 0.4816392662214463\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1970/1970 [00:08<00:00, 241.56it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5987781613460047, AUC: 0.6848767914604764, ACC: 0.6296785557023338, RMSE: 0.47870388217524784\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1970/1970 [00:08<00:00, 242.90it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5824070230353302, AUC: 0.6896485357245311, ACC: 0.6342286804638192, RMSE: 0.476797565397047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1970/1970 [00:08<00:00, 241.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5633742569515547, AUC: 0.6932740234201713, ACC: 0.6384118596800235, RMSE: 0.4758796280302964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1970/1970 [00:08<00:00, 243.31it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5407996147114613, AUC: 0.695556327438241, ACC: 0.6398062527520916, RMSE: 0.47740363865167945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 525/525 [00:00<00:00, 1002.25it/s]\n",
      "[I 2025-01-28 22:03:09,773] Trial 83 finished with value: 0.695556327438241 and parameters: {'batch_size': 52, 'learning_rate': 0.0009156353803623541}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001444\n",
      "Using device: cuda\n",
      "Seed: 1738072989\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 59\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1736/1736 [00:03<00:00, 455.64it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8932605841227116, AUC: 0.6496482705246139, ACC: 0.5140907089387935, RMSE: 0.5033077197169122\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1736/1736 [00:05<00:00, 289.79it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6958391740059797, AUC: 0.6658365154628261, ACC: 0.5140907089387935, RMSE: 0.5042912725042804\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1736/1736 [00:07<00:00, 245.20it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6640325740735102, AUC: 0.6710336037402245, ACC: 0.5589314545721414, RMSE: 0.49330601115258754\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1736/1736 [00:07<00:00, 244.90it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6228618219426151, AUC: 0.6793608380144891, ACC: 0.6138265081461911, RMSE: 0.48432244574002864\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5983419003402857, AUC: 0.6872227865805823, ACC: 0.6311463378834581, RMSE: 0.4797074663913377\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1736/1736 [00:07<00:00, 244.64it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5730928503800922, AUC: 0.692839796287654, ACC: 0.6378247468075737, RMSE: 0.4779432039954882\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1736/1736 [00:07<00:00, 244.66it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5391681998247101, AUC: 0.6954893914103902, ACC: 0.639292528988698, RMSE: 0.48128923160407966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1736/1736 [00:07<00:00, 244.40it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.4981180150319354, AUC: 0.6945310063442074, ACC: 0.6377513576985175, RMSE: 0.48623295496063157\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1736/1736 [00:07<00:00, 244.11it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.45396907339172976, AUC: 0.6895052415254342, ACC: 0.6337883458094818, RMSE: 0.49526946639274544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1736/1736 [00:07<00:00, 244.74it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.41414079112150975, AUC: 0.6832077352994348, ACC: 0.6332012329370321, RMSE: 0.5050970920818312\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 463/463 [00:00<00:00, 938.03it/s]\n",
      "[I 2025-01-28 22:04:20,739] Trial 84 finished with value: 0.6832077352994348 and parameters: {'batch_size': 59, 'learning_rate': 0.0014440950911706325}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002028\n",
      "Using device: cuda\n",
      "Seed: 1738073060\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 66\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.85902511831565, AUC: 0.6570420118712539, ACC: 0.5140907089387935, RMSE: 0.501310624439293\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6831566212295564, AUC: 0.6682230344239195, ACC: 0.5388228386907383, RMSE: 0.49770089790862077\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1552/1552 [00:06<00:00, 238.01it/s]\n",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6308209168695912, AUC: 0.6791045319970708, ACC: 0.6147805665639219, RMSE: 0.48402057754596656\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5992155686673737, AUC: 0.688881687525287, ACC: 0.6319536180830765, RMSE: 0.4794894354860143\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5634729641384071, AUC: 0.6948470793673278, ACC: 0.6373110230441802, RMSE: 0.47987893980424956\n"
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    },
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     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1552/1552 [00:06<00:00, 238.34it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5118786848159795, AUC: 0.6938168639333612, ACC: 0.6367239101717305, RMSE: 0.4856586806988585\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1552/1552 [00:06<00:00, 238.95it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.4531021794573087, AUC: 0.6870006115768819, ACC: 0.6315132834287391, RMSE: 0.4985917356605435\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1552/1552 [00:05<00:00, 262.89it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.4021399520030341, AUC: 0.6789193987378856, ACC: 0.6256421547042419, RMSE: 0.511391824297063\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1552/1552 [00:06<00:00, 245.92it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.3689287770587528, AUC: 0.6715799155694019, ACC: 0.6221928665785997, RMSE: 0.5218278950605113\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1552/1552 [00:06<00:00, 241.28it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3477940908393141, AUC: 0.6673448777228944, ACC: 0.6204315279612506, RMSE: 0.5270830170187422\n",
      "[NCDM] Early stopping at epoch 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 414/414 [00:00<00:00, 886.10it/s]\n",
      "[I 2025-01-28 22:05:28,949] Trial 85 finished with value: 0.6673448777228944 and parameters: {'batch_size': 66, 'learning_rate': 0.0020275643169694827}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000339\n",
      "Using device: cuda\n",
      "Seed: 1738073128\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 86\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1191/1191 [00:04<00:00, 279.46it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.7961179719914118, AUC: 0.5452498328755401, ACC: 0.5140907089387935, RMSE: 0.5025671639742048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1191/1191 [00:04<00:00, 238.65it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6934910486667323, AUC: 0.5883409815480151, ACC: 0.5140907089387935, RMSE: 0.5009467395751057\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1191/1191 [00:04<00:00, 239.50it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.692983194672691, AUC: 0.6184747102232735, ACC: 0.5140907089387935, RMSE: 0.5011676727905273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1191/1191 [00:04<00:00, 239.69it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6930881361316774, AUC: 0.6435165897101786, ACC: 0.5140907089387935, RMSE: 0.5011952491696486\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1191/1191 [00:05<00:00, 237.56it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6931006837852055, AUC: 0.6557999491376745, ACC: 0.5140907089387935, RMSE: 0.5010581253892691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1191/1191 [00:05<00:00, 237.52it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6929809344005025, AUC: 0.6613399861858873, ACC: 0.5140907089387935, RMSE: 0.5011065492010223\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1191/1191 [00:05<00:00, 238.11it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6927931365798643, AUC: 0.6643352144200623, ACC: 0.5140907089387935, RMSE: 0.501212070698869\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1191/1191 [00:04<00:00, 239.45it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6924483829141164, AUC: 0.6661664521889288, ACC: 0.5140907089387935, RMSE: 0.5010596607719917\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1191/1191 [00:04<00:00, 260.84it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6920823685067927, AUC: 0.6674401233028687, ACC: 0.5140907089387935, RMSE: 0.5009074845706232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1191/1191 [00:05<00:00, 237.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.69141411040632, AUC: 0.6684013759779113, ACC: 0.5140907089387935, RMSE: 0.5009516950605388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 318/318 [00:00<00:00, 686.92it/s]\n",
      "[I 2025-01-28 22:06:21,703] Trial 86 finished with value: 0.6684013759779113 and parameters: {'batch_size': 86, 'learning_rate': 0.00033884996902805903}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000564\n",
      "Using device: cuda\n",
      "Seed: 1738073181\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 40\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2560/2560 [00:10<00:00, 243.29it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.2653074015281163, AUC: 0.6202031991949997, ACC: 0.5140907089387935, RMSE: 0.5039785272232151\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2560/2560 [00:10<00:00, 242.41it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6967366845929064, AUC: 0.6619230486865004, ACC: 0.5140907089387935, RMSE: 0.5036714977151289\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2560/2560 [00:10<00:00, 242.70it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6960379592259415, AUC: 0.6670523169363243, ACC: 0.5140907089387935, RMSE: 0.503245336081206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 2560/2560 [00:10<00:00, 236.57it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6938603937975131, AUC: 0.6687820456637603, ACC: 0.5140907089387935, RMSE: 0.5029732818135363\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2560/2560 [00:10<00:00, 234.03it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6874521902180277, AUC: 0.6698890720493195, ACC: 0.5140907089387935, RMSE: 0.5008435779059612\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2560/2560 [00:10<00:00, 242.73it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6718200796400197, AUC: 0.6710747205078555, ACC: 0.5255394099515632, RMSE: 0.4965974037644692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2560/2560 [00:10<00:00, 242.41it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6481769715552218, AUC: 0.6731787907767781, ACC: 0.5623807426977836, RMSE: 0.48930359558636144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2560/2560 [00:10<00:00, 243.00it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.62627601622371, AUC: 0.6762483288901566, ACC: 0.5965800675179803, RMSE: 0.48425262645140654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2560/2560 [00:10<00:00, 240.78it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6103872215608135, AUC: 0.6800235467341007, ACC: 0.6203581388521944, RMSE: 0.48056006608810287\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2560/2560 [00:10<00:00, 241.81it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5978961894288659, AUC: 0.6839521924325097, ACC: 0.6292382210479964, RMSE: 0.47848746588163166\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 682/682 [00:00<00:00, 1126.29it/s]\n",
      "[I 2025-01-28 22:08:13,053] Trial 87 finished with value: 0.6839521924325097 and parameters: {'batch_size': 40, 'learning_rate': 0.0005638529532499667}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000728\n",
      "Using device: cuda\n",
      "Seed: 1738073293\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 55\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1862/1862 [00:07<00:00, 252.74it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0835606899316534, AUC: 0.6180531285989973, ACC: 0.5140907089387935, RMSE: 0.5049801662299972\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6963194296260141, AUC: 0.6602846091012515, ACC: 0.5140907089387935, RMSE: 0.5050161550706148\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1862/1862 [00:07<00:00, 240.17it/s]\n",
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6957730078863662, AUC: 0.6662322347049451, ACC: 0.5140907089387935, RMSE: 0.504729536617649\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6926007888306864, AUC: 0.6681694554162825, ACC: 0.5140907089387935, RMSE: 0.5039373451247213\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6815380277682324, AUC: 0.6694368846297352, ACC: 0.5141640980478497, RMSE: 0.5005770267596834\n"
     ]
    },
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     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1862/1862 [00:07<00:00, 253.58it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6575557847700365, AUC: 0.6716219918001479, ACC: 0.5468956406869221, RMSE: 0.4931475161831626\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6308001821618587, AUC: 0.6753429083439979, ACC: 0.5863789813591663, RMSE: 0.4855852640328228\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1862/1862 [00:07<00:00, 252.86it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6111720541270027, AUC: 0.6796655376265319, ACC: 0.618229854689564, RMSE: 0.48125357373822736\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1862/1862 [00:07<00:00, 253.52it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5968195965671642, AUC: 0.6841728581683891, ACC: 0.6283575517393218, RMSE: 0.4787354949478636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1862/1862 [00:07<00:00, 250.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.583383336423163, AUC: 0.6881899254001258, ACC: 0.6315132834287391, RMSE: 0.477667432432404\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 496/496 [00:00<00:00, 969.50it/s]\n",
      "[I 2025-01-28 22:09:32,431] Trial 88 finished with value: 0.6881899254001258 and parameters: {'batch_size': 55, 'learning_rate': 0.0007276077292408696}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000990\n",
      "Using device: cuda\n",
      "Seed: 1738073372\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 64\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1600/1600 [00:05<00:00, 286.65it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.4211674360372126, AUC: 0.6268164765905554, ACC: 0.5140907089387935, RMSE: 0.5055387242741112\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1600/1600 [00:06<00:00, 241.38it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6968971275538206, AUC: 0.6632407472988687, ACC: 0.5140907089387935, RMSE: 0.5055147649520324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1600/1600 [00:06<00:00, 241.83it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6944454973563552, AUC: 0.6673807875165441, ACC: 0.5140907089387935, RMSE: 0.5050012149620196\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1600/1600 [00:06<00:00, 241.42it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6813538811169565, AUC: 0.6691759106625568, ACC: 0.5162189931014237, RMSE: 0.49998077478851083\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1600/1600 [00:06<00:00, 241.49it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.648278109561652, AUC: 0.6728562279020283, ACC: 0.5670042565683253, RMSE: 0.4894105658308749\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1600/1600 [00:06<00:00, 244.09it/s]\n",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.618996100332588, AUC: 0.6786378232649537, ACC: 0.609716718039043, RMSE: 0.48248505225119076\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1600/1600 [00:06<00:00, 242.94it/s]\n",
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5996929792873561, AUC: 0.6845413135653746, ACC: 0.6275502715397036, RMSE: 0.4792540769157549\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1600/1600 [00:06<00:00, 241.22it/s]\n",
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     ]
    },
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5822788766585291, AUC: 0.6893379715289563, ACC: 0.6338617349185381, RMSE: 0.4775748007036026\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1600/1600 [00:06<00:00, 241.21it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5627100879140198, AUC: 0.6928644943774923, ACC: 0.6390723616615295, RMSE: 0.47680977075810826\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1600/1600 [00:06<00:00, 244.28it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5398257019370795, AUC: 0.6953004202987466, ACC: 0.6384118596800235, RMSE: 0.4779095159233803\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 427/427 [00:00<00:00, 903.12it/s]\n",
      "[I 2025-01-28 22:10:41,660] Trial 89 finished with value: 0.6953004202987466 and parameters: {'batch_size': 64, 'learning_rate': 0.0009904508494363584}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.002499\n",
      "Using device: cuda\n",
      "Seed: 1738073441\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 47\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2179/2179 [00:04<00:00, 476.53it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8496015800309323, AUC: 0.6632222264266112, ACC: 0.49251431087626596, RMSE: 0.5015048774411756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 2179/2179 [00:04<00:00, 474.48it/s]\n",
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6480136138548157, AUC: 0.6769424411609245, ACC: 0.6136063408190224, RMSE: 0.4815577289624507\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 2179/2179 [00:04<00:00, 472.40it/s]\n",
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    },
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.607706032949831, AUC: 0.6888741411861832, ACC: 0.6351093497724938, RMSE: 0.478351094915746\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.5612562364698167, AUC: 0.6948185110835777, ACC: 0.6395860854249229, RMSE: 0.4822501933079596\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 2179/2179 [00:05<00:00, 402.02it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.4894874713367801, AUC: 0.6888892985472974, ACC: 0.6335681784823132, RMSE: 0.4964937407775333\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 2179/2179 [00:06<00:00, 332.24it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.41748259984172226, AUC: 0.6772582231109654, ACC: 0.6274034933215911, RMSE: 0.5130606463776345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 2179/2179 [00:04<00:00, 468.06it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.37229958264829915, AUC: 0.6689298461053506, ACC: 0.62432115074123, RMSE: 0.5266101172178557\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2179/2179 [00:04<00:00, 468.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.34863019715815086, AUC: 0.6636753301873723, ACC: 0.6210186408337003, RMSE: 0.5326617594197605\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2179/2179 [00:04<00:00, 469.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.33418210055431186, AUC: 0.6589936999927015, ACC: 0.6194774695435198, RMSE: 0.5371320203470457\n",
      "[NCDM] Early stopping at epoch 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 581/581 [00:00<00:00, 1041.34it/s]\n",
      "[I 2025-01-28 22:11:30,396] Trial 90 finished with value: 0.6589936999927015 and parameters: {'batch_size': 47, 'learning_rate': 0.0024993986349494354}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001225\n",
      "Using device: cuda\n",
      "Seed: 1738073490\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 76\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.0792907270594592, AUC: 0.6431087747645245, ACC: 0.5140907089387935, RMSE: 0.5066838525667301\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6970189991973628, AUC: 0.6663744508555984, ACC: 0.5140907089387935, RMSE: 0.5067842610473074\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6913587881655651, AUC: 0.6693509210468583, ACC: 0.5140907089387935, RMSE: 0.5046655647444034\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6647449594532701, AUC: 0.672193228109337, ACC: 0.5478496991046529, RMSE: 0.4940508783727497\n"
     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6263486254595507, AUC: 0.6782052994489771, ACC: 0.6049464259503889, RMSE: 0.4840110175752439\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6024882264368966, AUC: 0.6849750663565768, ACC: 0.6289446646117716, RMSE: 0.4789938935148174\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5824576762645818, AUC: 0.6905109636987669, ACC: 0.6366505210626743, RMSE: 0.476781669899108\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1348/1348 [00:05<00:00, 262.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.560685741242563, AUC: 0.6945634771633225, ACC: 0.6399530309702041, RMSE: 0.47592123980496615\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1348/1348 [00:05<00:00, 245.77it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5327535579174492, AUC: 0.6964596134484817, ACC: 0.6388521943343608, RMSE: 0.47724200336547096\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1348/1348 [00:05<00:00, 245.22it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5006929811764541, AUC: 0.6960933896117742, ACC: 0.6375311903713489, RMSE: 0.48075321550395966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 359/359 [00:00<00:00, 820.30it/s]\n",
      "[I 2025-01-28 22:12:29,147] Trial 91 finished with value: 0.6960933896117742 and parameters: {'batch_size': 76, 'learning_rate': 0.0012250927305869504}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001660\n",
      "Using device: cuda\n",
      "Seed: 1738073549\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 76\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1348/1348 [00:04<00:00, 279.56it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.8865309233614175, AUC: 0.653729190565653, ACC: 0.5140907089387935, RMSE: 0.5073341318253706\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1348/1348 [00:04<00:00, 273.33it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6955695148966433, AUC: 0.6672682931614752, ACC: 0.5140907089387935, RMSE: 0.5047729905395492\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1348/1348 [00:05<00:00, 232.25it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6623913732409831, AUC: 0.6723694459078952, ACC: 0.5642154704241891, RMSE: 0.4917807190194928\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1348/1348 [00:05<00:00, 237.91it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6197248558660289, AUC: 0.6814086772766037, ACC: 0.6202847497431381, RMSE: 0.4830142851077709\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1348/1348 [00:05<00:00, 237.68it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.5938564330914254, AUC: 0.6893385752360844, ACC: 0.6325407309555262, RMSE: 0.47875080127993047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1348/1348 [00:05<00:00, 244.62it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5648996683185815, AUC: 0.6944958188430147, ACC: 0.6383384705709673, RMSE: 0.4775020180214953\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1348/1348 [00:05<00:00, 260.90it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5259649193339956, AUC: 0.6962286200085145, ACC: 0.6395860854249229, RMSE: 0.4800124338404734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1348/1348 [00:04<00:00, 274.12it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.4814566215970042, AUC: 0.6933468024705851, ACC: 0.6379715250256862, RMSE: 0.4878544155714037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1348/1348 [00:04<00:00, 273.84it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.4370277798693152, AUC: 0.6873399273244423, ACC: 0.6328342873917511, RMSE: 0.49758804975703\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1348/1348 [00:05<00:00, 263.15it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.3977102555931499, AUC: 0.6803832699386946, ACC: 0.6277704388668721, RMSE: 0.5080897188388183\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 359/359 [00:00<00:00, 819.69it/s]\n",
      "[I 2025-01-28 22:13:25,607] Trial 92 finished with value: 0.6803832699386946 and parameters: {'batch_size': 76, 'learning_rate': 0.0016603903346570916}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001398\n",
      "Using device: cuda\n",
      "Seed: 1738073605\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 97\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1056/1056 [00:02<00:00, 390.74it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 0.9858470462601293, AUC: 0.6271187505936004, ACC: 0.5140907089387935, RMSE: 0.5054164988158145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1056/1056 [00:02<00:00, 391.88it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6964065223706492, AUC: 0.6613306502863674, ACC: 0.5140907089387935, RMSE: 0.5056970143766075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1056/1056 [00:02<00:00, 357.35it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6909069913354787, AUC: 0.6663245264321846, ACC: 0.5140907089387935, RMSE: 0.5036459698720499\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1056/1056 [00:04<00:00, 239.21it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6637130425960729, AUC: 0.6703205501583059, ACC: 0.5551886100102745, RMSE: 0.49307783331589417\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1056/1056 [00:04<00:00, 239.06it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6238518443918137, AUC: 0.6775991128092531, ACC: 0.6160281814178776, RMSE: 0.48201280155670456\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1056/1056 [00:04<00:00, 239.20it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.5989128388464451, AUC: 0.6849113968155096, ACC: 0.6301922794657273, RMSE: 0.4777759699437007\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1056/1056 [00:04<00:00, 239.08it/s]\n",
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    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5771770796766786, AUC: 0.6905995577198456, ACC: 0.637604579480405, RMSE: 0.47588700099049835\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1056/1056 [00:04<00:00, 239.19it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.551447311246937, AUC: 0.6941371629063798, ACC: 0.6391457507705857, RMSE: 0.47666414425228043\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1056/1056 [00:04<00:00, 238.87it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5209873773044709, AUC: 0.6953498056979388, ACC: 0.638925583443417, RMSE: 0.47869681872998926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1056/1056 [00:04<00:00, 239.23it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.487253522980168, AUC: 0.6942543791136307, ACC: 0.6359900190811684, RMSE: 0.48422541403650876\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 282/282 [00:00<00:00, 711.52it/s]\n",
      "[I 2025-01-28 22:14:08,596] Trial 93 finished with value: 0.6942543791136307 and parameters: {'batch_size': 97, 'learning_rate': 0.0013981328242780467}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000404\n",
      "Using device: cuda\n",
      "Seed: 1738073648\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 51\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 2008/2008 [00:04<00:00, 402.13it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.562748075407221, AUC: 0.5888692252852812, ACC: 0.5140907089387935, RMSE: 0.5022624408264988\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6941447970104883, AUC: 0.6326532464728142, ACC: 0.5140907089387935, RMSE: 0.5027036633567001\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6944381396074694, AUC: 0.6572869768190478, ACC: 0.5140907089387935, RMSE: 0.5027366172116723\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.694480756691015, AUC: 0.6635575102729933, ACC: 0.5140907089387935, RMSE: 0.502756173707407\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6941147763653105, AUC: 0.6662234809515848, ACC: 0.5140907089387935, RMSE: 0.5027778887213836\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6933674719704099, AUC: 0.6676727445959858, ACC: 0.5140907089387935, RMSE: 0.5024594588918244\n"
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6917298016735757, AUC: 0.6686746181363754, ACC: 0.5140907089387935, RMSE: 0.5022604792754624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 2008/2008 [00:08<00:00, 241.56it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6887759355850429, AUC: 0.6694203473666134, ACC: 0.5140907089387935, RMSE: 0.5014492100173665\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 2008/2008 [00:07<00:00, 260.53it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6828845172111256, AUC: 0.6700461652684918, ACC: 0.5140907089387935, RMSE: 0.4998333014252132\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 2008/2008 [00:08<00:00, 239.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6726424438782422, AUC: 0.670821562391892, ACC: 0.5176133861734918, RMSE: 0.49682449816444374\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 535/535 [00:00<00:00, 734.56it/s]\n",
      "[I 2025-01-28 22:15:28,349] Trial 94 finished with value: 0.670821562391892 and parameters: {'batch_size': 51, 'learning_rate': 0.00040407410635955114}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000987\n",
      "Using device: cuda\n",
      "Seed: 1738073728\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 57\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1797/1797 [00:07<00:00, 235.85it/s]\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.1336630467713111, AUC: 0.6307429446311086, ACC: 0.5140907089387935, RMSE: 0.506153536743211\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1797/1797 [00:07<00:00, 233.51it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6975874841445409, AUC: 0.6627550972556013, ACC: 0.5140907089387935, RMSE: 0.5055050722101345\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1797/1797 [00:05<00:00, 311.38it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6932264022284504, AUC: 0.666921582001593, ACC: 0.5140907089387935, RMSE: 0.5042133592431868\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1797/1797 [00:07<00:00, 244.97it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6732063250669056, AUC: 0.669539816695111, ACC: 0.5336856010568032, RMSE: 0.49623254790139376\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1797/1797 [00:07<00:00, 242.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6382276968917517, AUC: 0.6742425982002412, ACC: 0.5780860120358139, RMSE: 0.48727569499379486\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1797/1797 [00:07<00:00, 238.30it/s]\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6122401798176116, AUC: 0.6801338418703451, ACC: 0.6237340378687802, RMSE: 0.48055532653786787\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1797/1797 [00:07<00:00, 237.10it/s]\n",
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    },
    {
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5952499731089848, AUC: 0.685898382506896, ACC: 0.6309995596653457, RMSE: 0.4777636784062272\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1797/1797 [00:07<00:00, 241.34it/s]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5770504295527438, AUC: 0.6903906211510301, ACC: 0.636210186408337, RMSE: 0.47702235314657726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1797/1797 [00:07<00:00, 234.42it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5560861554786638, AUC: 0.6935975888799735, ACC: 0.6382650814619111, RMSE: 0.4768077910566594\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1797/1797 [00:07<00:00, 236.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.5315050097160626, AUC: 0.6952352522703431, ACC: 0.6415675913694407, RMSE: 0.4778829814350713\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 479/479 [00:00<00:00, 952.29it/s]\n",
      "[I 2025-01-28 22:16:46,598] Trial 95 finished with value: 0.6952352522703431 and parameters: {'batch_size': 57, 'learning_rate': 0.000987163124240284}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000724\n",
      "Using device: cuda\n",
      "Seed: 1738073806\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 85\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1205/1205 [00:04<00:00, 259.47it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.3138141130016059, AUC: 0.5977939032263941, ACC: 0.5140907089387935, RMSE: 0.502039317934316\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1205/1205 [00:05<00:00, 239.78it/s]\n",
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     ]
    },
    {
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.694981808187556, AUC: 0.6421860407603648, ACC: 0.5140907089387935, RMSE: 0.5029772545650888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1205/1205 [00:05<00:00, 239.89it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6950711979410955, AUC: 0.661842087248401, ACC: 0.5140907089387935, RMSE: 0.5029755686165446\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6942103677765462, AUC: 0.66631213965557, ACC: 0.5140907089387935, RMSE: 0.5028940231642419\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6915554174743748, AUC: 0.6685560543685704, ACC: 0.5140907089387935, RMSE: 0.5023990313102227\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1205/1205 [00:05<00:00, 239.29it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6848664562237213, AUC: 0.669856946205706, ACC: 0.5140907089387935, RMSE: 0.5004804585814585\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6701858838564133, AUC: 0.6714165157668358, ACC: 0.5257595772787318, RMSE: 0.49598777601719446\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1205/1205 [00:04<00:00, 251.46it/s]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6462063441880016, AUC: 0.6736691518917433, ACC: 0.5639953030970204, RMSE: 0.4894812343955592\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1205/1205 [00:04<00:00, 276.22it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.623858140093657, AUC: 0.6768601645037242, ACC: 0.5955526199911934, RMSE: 0.4836955745566999\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1205/1205 [00:05<00:00, 238.83it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6075494290634804, AUC: 0.6806028899675842, ACC: 0.6191839131072949, RMSE: 0.4806212179279798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 321/321 [00:00<00:00, 766.59it/s]\n",
      "[I 2025-01-28 22:17:39,486] Trial 96 finished with value: 0.6806028899675842 and parameters: {'batch_size': 85, 'learning_rate': 0.0007236979504585026}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000525\n",
      "Using device: cuda\n",
      "Seed: 1738073859\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 66\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 0, Loss: 1.3004311984500934, AUC: 0.584790116365627, ACC: 0.5140907089387935, RMSE: 0.5025336923129211\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6945373335680396, AUC: 0.6320716285571152, ACC: 0.5140907089387935, RMSE: 0.502891574353273\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6948233449966023, AUC: 0.6567605226421975, ACC: 0.5140907089387935, RMSE: 0.5027925561171355\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6944871717276647, AUC: 0.6633375452686017, ACC: 0.5140907089387935, RMSE: 0.5027755694528019\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6935602928482995, AUC: 0.666379927341691, ACC: 0.5140907089387935, RMSE: 0.5024524529719104\n"
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    },
    {
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     "output_type": "stream",
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     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6916720615603875, AUC: 0.6679957602510819, ACC: 0.5140907089387935, RMSE: 0.5023544759132997\n"
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     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1552/1552 [00:06<00:00, 236.60it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 863.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6872165422003294, AUC: 0.6691077995619028, ACC: 0.5140907089387935, RMSE: 0.5011394130620993\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1552/1552 [00:06<00:00, 237.75it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 854.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6783778191995375, AUC: 0.6700599966300206, ACC: 0.5143842653750184, RMSE: 0.49889068152122196\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1552/1552 [00:06<00:00, 238.22it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 858.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6632328323021377, AUC: 0.6712838942473287, ACC: 0.5355203287832085, RMSE: 0.49447231823645404\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1552/1552 [00:06<00:00, 238.53it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 207/207 [00:00<00:00, 862.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6449160500400767, AUC: 0.6729939809321259, ACC: 0.5639953030970204, RMSE: 0.4889623699752097\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 414/414 [00:00<00:00, 884.79it/s]\n",
      "[I 2025-01-28 22:18:48,742] Trial 97 finished with value: 0.6729939809321259 and parameters: {'batch_size': 66, 'learning_rate': 0.0005246485293642944}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.001223\n",
      "Using device: cuda\n",
      "Seed: 1738073928\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 72\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1423/1423 [00:03<00:00, 434.79it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 853.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 1.1635671935279186, AUC: 0.6325759935213601, ACC: 0.5140907089387935, RMSE: 0.5072275913095844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1423/1423 [00:05<00:00, 274.72it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 832.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6971763739766826, AUC: 0.6631147126553508, ACC: 0.5140907089387935, RMSE: 0.5068266222922512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1423/1423 [00:06<00:00, 236.07it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 828.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6902877078329436, AUC: 0.667301820468065, ACC: 0.5140907089387935, RMSE: 0.5040857446780086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1423/1423 [00:05<00:00, 240.01it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 829.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6606693924185431, AUC: 0.6714285144460108, ACC: 0.5554087773374431, RMSE: 0.4930633873175406\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1423/1423 [00:05<00:00, 239.51it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 825.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6235217984218624, AUC: 0.6780015914151121, ACC: 0.6068545427858506, RMSE: 0.48449683326433457\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1423/1423 [00:05<00:00, 240.06it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 831.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6002667900423558, AUC: 0.6849199457396657, ACC: 0.6293849992661089, RMSE: 0.4800556373343961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1423/1423 [00:05<00:00, 242.61it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 831.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.5799773652329047, AUC: 0.6903729195955896, ACC: 0.6352561279906062, RMSE: 0.477218541686865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1423/1423 [00:05<00:00, 243.06it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 830.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.5567634308204128, AUC: 0.6941145670110062, ACC: 0.6375311903713489, RMSE: 0.47712829074623575\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1423/1423 [00:05<00:00, 243.62it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 829.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.5272474275117849, AUC: 0.6957148113399053, ACC: 0.6386320270071921, RMSE: 0.47969180382163307\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1423/1423 [00:05<00:00, 243.64it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 190/190 [00:00<00:00, 826.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.4950208124650337, AUC: 0.695231177247227, ACC: 0.6378247468075737, RMSE: 0.4828732346791433\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 379/379 [00:00<00:00, 843.93it/s]\n",
      "[I 2025-01-28 22:19:48,493] Trial 98 finished with value: 0.695231177247227 and parameters: {'batch_size': 72, 'learning_rate': 0.0012226100348160013}. Best is trial 81 with value: 0.696368680062281.\n",
      "/tmp/ipykernel_3917539/4236103119.py:11: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n",
      "  learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting LR: 0.000265\n",
      "Using device: cuda\n",
      "Seed: 1738073988\n",
      "Dataset: assistment-2017\n",
      "Dataset Length: train 102397, test 27275, valid 13626\n",
      "Batch size: 88\n",
      "Preparing data\n",
      "Data loaded\n",
      "Model loaded\n",
      "[NCDM] Early stopping enabled\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 0: 100%|██████████| 1164/1164 [00:04<00:00, 244.17it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 734.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 0, Loss: 2.370160688700545, AUC: 0.5226730836422211, ACC: 0.5140907089387935, RMSE: 0.5046249768685171\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 1: 100%|██████████| 1164/1164 [00:04<00:00, 246.80it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 743.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 1, Loss: 0.6943544204702082, AUC: 0.5512377990519858, ACC: 0.5140907089387935, RMSE: 0.50069592587041\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 2: 100%|██████████| 1164/1164 [00:04<00:00, 240.69it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 740.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 2, Loss: 0.6927754070955453, AUC: 0.5903021241543114, ACC: 0.5140907089387935, RMSE: 0.5008870942991086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 3: 100%|██████████| 1164/1164 [00:04<00:00, 247.05it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 748.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 3, Loss: 0.6927980196332604, AUC: 0.6222762755711744, ACC: 0.5140907089387935, RMSE: 0.501015378594188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 4: 100%|██████████| 1164/1164 [00:04<00:00, 246.25it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 739.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 4, Loss: 0.6928269758666914, AUC: 0.6438206748346085, ACC: 0.5140907089387935, RMSE: 0.5009998579806046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 5: 100%|██████████| 1164/1164 [00:04<00:00, 242.13it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 741.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 5, Loss: 0.6927926025234956, AUC: 0.6548239487599263, ACC: 0.5140907089387935, RMSE: 0.5011030147135155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 6: 100%|██████████| 1164/1164 [00:04<00:00, 247.09it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 743.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 6, Loss: 0.6927641879549551, AUC: 0.6606623896172723, ACC: 0.5140907089387935, RMSE: 0.5010711813926836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 7: 100%|██████████| 1164/1164 [00:04<00:00, 247.42it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 745.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 7, Loss: 0.6925595348950514, AUC: 0.6635386659862025, ACC: 0.5140907089387935, RMSE: 0.5010564211834396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 8: 100%|██████████| 1164/1164 [00:04<00:00, 247.15it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 745.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 8, Loss: 0.6923946986083722, AUC: 0.6652829268066555, ACC: 0.5140907089387935, RMSE: 0.5010699925928197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch 9: 100%|██████████| 1164/1164 [00:04<00:00, 247.08it/s]\n",
      "[NCDM] evaluating: 100%|██████████| 155/155 [00:00<00:00, 745.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[NCDM] Epoch: 9, Loss: 0.6922270235736755, AUC: 0.6666117940008978, ACC: 0.5140907089387935, RMSE: 0.5009006498626203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[NCDM] evaluating: 100%|██████████| 310/310 [00:00<00:00, 760.81it/s]\n",
      "[I 2025-01-28 22:20:39,605] Trial 99 finished with value: 0.6666117940008978 and parameters: {'batch_size': 88, 'learning_rate': 0.00026519048054887944}. Best is trial 81 with value: 0.696368680062281.\n"
     ]
    }
   ],
   "source": [
    "DATASET = \"ASSIST2017\"\n",
    "# MODEL = \"DINA\"\n",
    "# MODEL = \"MIRT\"\n",
    "MODEL = \"NCDM\"\n",
    "\n",
    "study = optuna.create_study(direction='maximize')\n",
    "study.optimize(lambda trial: objective(trial, MODEL, DATASET), n_trials=100)\n",
    "\n",
    "trial = study.best_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'batch_size': 61, 'learning_rate': 0.0010067931759100532}\n"
     ]
    }
   ],
   "source": [
    "print(trial.params)"
   ]
  }
 ],
 "metadata": {
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   "display_name": "wxy-cognitive",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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